=Paper=
{{Paper
|id=Vol-3861/paper6
|storemode=property
|title=Smart system for passive detection and classification of mines using feed-forward deep neural networks
|pdfUrl=https://ceur-ws.org/Vol-3861/paper6.pdf
|volume=Vol-3861
|authors=Vasyl Lytvyn,Roman Peleshchak,Victoria Vysotska,Ivan Peleshchak,Lyubomyr Chyrun,Mariia Nazarkevych,Serhii Vladov,Olga Lozynska,Serhii Voloshyn,Olena Nagachevska
|dblpUrl=https://dblp.org/rec/conf/ciaw/LytvynPVPCNVLVN24
}}
==Smart system for passive detection and classification of mines using feed-forward deep neural networks==
https://ceur-ws.org/Vol-3861/paper6.pdf
⋆
Vasyl Lytvyn1,†, Roman Peleshchak1,∗,†, Victoria Vysotska1,†, Ivan Peleshchak1,∗,†, Lyubomyr
Chyrun2,†, Mariia Nazarkevych1,†, Serhii Vladov3,†, Olga Lozynska1,†, Serhii Voloshyn1,†, and
Olena Nagachevska1,†
1
Lviv Polytechnic National University, Stepan Bandera 12, 79013 Lviv, Ukraine
2
Ivan Franko National University of Lviv, University 1, 79000 Lviv, Ukraine
3
Kremenchuk Flight College of Kharkiv National University of Internal Affairs, Peremohy Street 17/6 39605 Kremenchuk,
Ukraine
Abstract
The ongoing war waged by russia against Ukraine has accelerated the development of advanced
technologies, including self-propelled artillery systems with integrated software, drones for enemy
identification, and the widespread use of Starlink for internet connectivity in areas with limited access. A
significant challenge facing Ukraine is demining territories liberated from temporary occupation. Official
estimates indicate that over 30% of these areas are contaminated with explosive remnants of war, with 2.6
million hectares of agricultural land requiring urgent demining, severely disrupting the country's agrarian
economy. Safely detecting, classifying, and neutralising these mines without risking human lives remains
a pressing issue. Current detection methods rely on active sensors like ultra-wideband (UWB) radar.
Although effective, these systems can inadvertently trigger mine explosions due to transmitted and
reflected electromagnetic signals. In contrast, passive detection methods that do not activate detonating
mechanisms offer a safer alternative. This research presents a sophisticated system for the passive detection
and classification of mines using deep neural networks. Two models were developed: one with a single
hidden layer and another with two hidden layers, achieving accuracies of 97.9% and 99.2%, respectively.
The two-hidden-layer model demonstrated superior performance, surpassing a comparable k-NN heuristic
algorithm by 1% in classification accuracy. Key advancements include reduced misclassifications, improved
training efficiency, enhanced ROC curve performance, and an AUC exceeding 0.99, indicating exceptional
efficacy in differentiating mine types. The F1 score of over 0.8 reflects the model's reliability, while loss
metrics below 0.1 underscore the effectiveness of the training process. Recommendations for future work
include developing datasets based on empirical data to enhance robustness and exploring parameter
optimisation using more powerful hardware.
Keywords
mine detection, deep neural networks, passive sensors, demining technology, explosive remnants of war,
classification accuracy, ROC curve, AUC value, F1 score, training efficiency.1
1. Introduction
The detection of landmines remains a persistent and escalating global challenge, endangering
millions of people due to the lethal threat posed by these explosive devices. In 2016, an average of 23
individuals per day worldwide were killed or severely injured by landmines or other explosive
CIAW-2024: Computational Intelligence Application Workshop, October 10-12, 2024, Lviv, Ukraine
∗
Corresponding author.
†
These authors contributed equally.
Vasyl.V.Lytvyn@lpnu.ua (V. Lytvyn); roman.m.peleshchak@lpnu.ua (R. Peleshchak); Victoria.A.Vysotska@lpnu.ua (V.
Vysotska); ivan.r.peleshchak@lpnu.ua (I. Peleshchak); Lyubomyr.Chyrun@lnu.edu.ua (L. Chyrun);
mariia.a.nazarkevych@lpnu.ua (M. Nazarkevych); serhii.vladov@univd.edu.ua (S. Vladov); Olha.V.Lozynska@lpnu.ua (O.
Lozynska); serhii.voloshyn.msaad.2022@lpnu.ua (S. Voloshyn); olena.o.nahachevska@lpnu.ua (O. Nagachevska)
0000-0002-9676-0180 (V. Lytvyn); 0000-0002-0536-3252 (R. Peleshchak); 0000-0001-6417-3689 (V. Vysotska); 0000-0002-
7481-8628 (I. Peleshchak); 0000-0002-9448-1751 (L. Chyrun); 0000-0002-6528-9867 (M. Nazarkevych); 0000-0001-8009-5254
(S. Vladov); 0000-0002-5079-0544 (O. Lozynska); 0000-0002-5393-008X (S. Voloshyn); 0000-0002-5200-8085 (O.
Nagachevska)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
remnants of war. Currently, approximately 61 countries and territories remain contaminated by
landmines, with thousands continuing to live under the daily threat of injury or death from these
hidden dangers [1]. Traditional landmine detection and identification methods are no longer
sufficiently reliable or efficient, necessitating the adoption of modern automated tools, such as neural
networks. Developing a passive system for detecting and classifying landmines with high accuracy
using neural networks and magnetic field sensors is an urgent priority. Russia's war against Ukraine
has further accelerated the deployment of innovative technologies by Ukrainian forces, including:
Cyber Attacks. Ukraine swiftly migrated its digital infrastructure to the public cloud, hosted
across European data centres. Through collaborations with international tech companies
such as Cloudflare and Microsoft, Ukraine has bolstered the resilience of its encryption and
systems.
Satellites. Approximately 25,000 Starlink terminals have been deployed, supporting military
operations and Ukrainian civilians deprived of internet access. Other commercial space
enterprises have contributed to Ukraine's military efforts through remote sensing and
satellite communications. For instance, Ukrainian entrepreneur Serhiy Prytula facilitated the
acquisition of a satellite and access to ICEYE's data repository.
Drones. Unmanned aerial vehicles (UAVs), including Bayraktar, Furia, and Valkyrie, are used
for reconnaissance and strike missions. Civilian drones have also been widely repurposed for
reconnaissance.
Artificial Intelligence (AI). Ukrainian AI firm Primer has adapted AI-powered speech
transcription and translation services to process intercepted Russian communications,
automatically highlighting intelligence on Ukrainian forces. AI-powered facial recognition
technology from Clearview AI has also been employed to identify deceased Russian
personnel through their social media profiles [2].
A critical issue for Ukraine is the demining of liberated territories formerly under occupation.
The State Emergency Service of Ukraine reported via its official Telegram channel that
approximately 175,000 square kilometres of territory remain potentially hazardous due to explosive
remnants of war – equivalent to 30% of the country's total land area. Moreover, 2.6 million hectares
of agricultural land require demining due to the Russian invasion, significantly threatening Ukraine's
agricultural output. Landmines pose a substantial obstacle to Ukraine's post-conflict reconstruction.
Even before the full-scale invasion, an estimated 1.8 million Ukrainians lived in mine-affected areas
since 2014, according to the United Nations Office for the Coordination of Humanitarian Affairs in
Ukraine [3].
The ongoing war has triggered a large-scale humanitarian crisis, including the widespread use of
landmines and other explosive devices, resulting in the fastest-growing refugee population since
World War II. Anti-personnel and anti-vehicle mines, along with unexploded ordnance in Ukraine,
present a severe threat to millions of people. Clearing these mines will take years, hindering
reconstruction efforts and endangering displaced persons returning to their homes. While large-scale
demining is not feasible during the conflict, efforts are underway to coordinate support for Ukrainian
authorities in identifying, locating, and removing explosive devices wherever possible [4].
Landmine detection uses various methods, many of which employ active sensors. However, active
sensors may inadvertently trigger mine explosions because they rely on transmitted and reflected
signals. Passive detection methods, which do not activate detonating mechanisms, offer a safer
alternative. Nonetheless, passive detectors are typically less effective than their active counterparts.
Studies indicate that machine learning algorithms can significantly enhance their performance.
This project envisions the safe detection and neutralisation of landmines. The system is designed
to disarm mines during military operations and clear mined areas after they are liberated. On a global
scale, this system could be utilised to clear mine-contaminated areas resulting from past conflicts,
including those dating back to World War II. The primary objective of this work is the efficient
detection and classification of landmines using a neural network. The system will classify mines
based on input data, which includes a vector of three values: voltage, height above ground, and soil
type. The output will display the most likely mine type.
The work aims to develop an optimal neural network structure for effectively recognising
different types of mines depending on the soil structure. The system's main task is the classification
of mines using a deep artificial neural network of direct propagation. The object of research is the
process of classifying mines of different types located in soils with other structures. The research
subject is the methods and means of creating a neural network system for classifying mines during
the execution of a combat mission or demining by public protection services. In particular, the mine
classification process is investigated using a forward propagation artificial neural network with one
and two hidden layers. The task of the work is to develop an optimal neural network system of direct
propagation for recognising different types of mines located in various soils, using data from
magnetic field sensors. For the successful development of the system, the following sequence of tasks
was formed:
1. The first task involves the analysis of the current state and prospects in the field of detection
and recognition of mines of various types by neural networks. It is necessary to analyse
scientific publications that are freely available on the Internet and have similar functionality.
2. The second task involves system analysis and modelling of the neural network system. The
result is a formalised unified description of business processes, project requirements, risks,
objects of information, material, resource flows and other project components.
3. Project development involves formulation of the problem, construction of models to solve
the problem, description of the methods used, selection of development tools, and direct
development of the system. The result of the task is a fully or partially finished software
product, which is supposed to solve the given problem.
4. Project testing involves the analysis of execution results, their evaluation, variation and
validation. Deployment consists of developing a documented description of actions related
to installing the system and its operation. The result of the implementation is a neural
network system that has undergone verification and validation.
The innovative contribution of this research lies in developing a complete system that allows safe
and accurate detection and classification of mines buried in the ground. The described approach
involves detecting and recognising mines located in soils with different structures using magnetic
field sensors and a deep neural network of direct propagation.
2. Related works
2.1. Analytical review of research and development in military technologies
utilising artificial intelligence
Mine detection and classification systems are software solutions integrated into sensors, robotics,
drones, or vehicles. Landmine detection and classification systems are software solutions
increasingly integrated into advanced technological solutions, including sensors, robotics,
unmanned aerial vehicles (UAVs), and autonomous ground vehicles. A modern approach to
landmine detection challenges incorporates machine learning techniques such as computer vision,
classification algorithms, and deep learning. These methods significantly enhance detection accuracy
by improving upon traditional technologies, which would otherwise be inadequate without
sophisticated software integration. This review concludes with a comparative analysis of existing
systems alongside our proposed solution, followed by critical conclusions drawn from this
comparison. One of the significant advancements in this field is using unmanned aerial vehicles
(UAVs) for landmine detection. Recent progress in UAV-based remote sensing, employing
lightweight multispectral and thermal infrared sensors, has rapidly detected landmine contamination
across large areas, facilitating efficient mapping and detection efforts. Researchers at Binghamton
University have focused on developing and testing automated remote detection techniques for anti-
personnel mines, particularly in identifying scattered anti-personnel landmines. Their study
highlights the severe and long-lasting humanitarian and economic threats posed by the remnants of
scattered plastic mines, such as PFM-1, which continue to affect communities in post-conflict
regions. The methodology employed by these researchers is particularly suited for detecting plastic
landmines containing liquid explosives encased in non-metallic materials such as polyethene or
plastic. The system leverages multispectral and thermal datasets collected via an automated UAV
imaging system, with PFM-1-type landmines serving as test subjects. The research team sought to
automate landmine detection using supervised learning algorithms, precisely the Faster Regional-
Convolutional Neural Network (Faster R-CNN). Their trials using RGB-visible light imaging
combined with Faster R-CNN resulted in a detection accuracy of 99.3% for partially concealed
landmines, while fully concealed landmines were detected with 71.5% accuracy.
In several test scenarios, combining centimetre-scale georeferencing datasets with the Faster R-
CNN algorithm enabled the accurate autonomous detection of test PFM-1 landmines. The potential
of this approach extends to humanitarian demining operations, with the capability to calibrate the
method for detecting other types of scattered anti-personnel mines. It could be crucial in demining
efforts in various post-conflict areas worldwide. The research collected field data under different
environmental conditions to best model real-world scenarios. These conditions included sparse
vegetation in Chenango Valley State Park, agricultural and pasture fields at Binghamton University,
and snow-covered terrain after three inches of snowfall. The collected datasets are proxies for
minefields under desert, farming, and winter conditions, respectively. While these environments
may not perfectly replicate real minefields, they offer reliable spectral analogues, allowing for
comprehensive testing of the detection system across various landscapes Data was collected using
advanced sensor technology, including the FLIR Vue Pro thermal infrared sensor and the Parrot
Sequoia multispectral sensor, mounted on a DJI Matrice 600 Pro UAV platform. Ground control
points (GCPs) were strategically placed at grid intersections, and precise geospatial data was
gathered using the Trimble Geo 7x handheld global navigation satellite system (GNSS). The UAV
executed flight missions over simulated minefields, each containing 28–30 PFM-1 landmines
scattered randomly within the grid to simulate real-world conditions. Multiple flight paths ensured
the collection of extensive datasets later used for training and testing the CNN model. The
convolutional neural network (CNN) employed for mine detection processed data with an average
time of 1.87 seconds to detect PFM-1 landmines within a 10 × 20 m minefield. Scaling this to larger
areas, the system could scan one square kilometre in approximately two hours and 36 minutes,
achieving an overall mine detection accuracy of 71.5%. Each UAV flight covered a 10 × 20 m area in
3 minutes and 30 seconds. While the system demonstrated promising results, particularly with
partially concealed landmines, the researchers acknowledged the need for further improvements to
enhance detection accuracy for fully concealed mines. Detailed results of their findings are presented
in Table 1 [5].
Table 1
Detailed Research Results
Training Training Test Data Test Average Accuracy
Sample Time Time PFM- KSF- Both
1 Casing Mines
Six flights, grass & 37 One flight rubble 1.87 0.7030 0.7273 0.7152
rubble (Fall 2019) (Fall 2017)
Random 70% of 7 total 29 Random 30% of 7 5.47 0.9983 0.9879 0.9931
flights total flights
In the study [6], an experiment was carried out to generate data for passive mine detection and
classify mines based on their magnetic field anomaly characteristics, soil depth, and soil type. This
approach was designed to identify the specific type of mine by analysing variations in magnetic
anomalies. The method relies on three independent variables (input parameters): the type of soil
() in which the mine is buried, the height of the detector above the ground (), and the
magnitude of the magnetic anomaly (). A ferroprobe sensor was utilised to measure these
magnetic anomalies. The detection process begins by determining whether the buried object is a
mine. If a mine is identified, its type is then classified based on analysing the magnetic anomaly
caused by the buried object. The classification considers the soil type and the distance between the
sensor and the ground. Advanced machine learning algorithms are employed to classify the type of
mine, where the mine type () is expressed as a function of the three independent variables:
Mtype = f (V, H, S). The study further analysed the relationship between magnetic field anomalies and
various environmental factors, including soil type (Figure 1) and depth (Figure 2), identifying specific
magnetic anomalies associated with different mine types (Figure 3) [6]. This detailed analysis
enhances the precision of mine classification and provides valuable insights into how magnetic field
anomalies vary depending on the surrounding environmental conditions.
Figure 1: Magnetic field anomaly values relative to soil type for each mine type [6]
Figure 2: Magnetic field anomaly values relative to depth for each mine type [6]
Figure 3: Graphs of the training dataset: a – Absence of a mine, b – Anti-tank mine, c – Anti-
personnel mine, d – Booby trap mine, e – M14 mine, where x – sensor position (cm) and y – anomaly
voltage (V) [6]
Having established the dataset, the researchers developed several machine learning algorithms,
including models based on artificial neural networks (ANN) and k-nearest neighbours (k-NN), to
address the problem of mine detection and classification. After conducting a series of experiments,
they determined that the heuristic k-NN algorithm, enhanced with fuzzy metrics, was the most
effective among the developed models. This approach achieved a mine detection efficiency of 98.2%,
outperforming the other models. In contrast, the artificial neural network-based model demonstrated
an average success rate of 95.6%, with an error rate of 4.4% [6].
2.2. Comparison of existing products
The "Landmine Detection Robot" is an advanced robotic system designed to identify landmines using
integrated sensors and relay GPS coordinates of detected mines to a server, where an updated
minefield map is maintained. Deploying multiple robots for mine detection enhances the
identification of safe paths, a process conducted entirely autonomously without human involvement.
The developers highlight that conventional mine clearance methods, such as manual tools, human-
operated metal detectors, or machinery, are labour-intensive, costly, time-consuming, and pose
substantial risks to personnel and equipment. The project aims to provide more efficient and
sophisticated solutions for detecting, locating, and neutralising landmines, improving safety in
affected areas. The system comprises three primary components: a web application, a robot, and a
web server. The web application, deployed using Amplify Serverless methods, serves as a user access
point within a designated user group. Upon logging in, users can access the main control page for
managing a specific robot or the administrator page if they belong to an authorised user group. The
robot's control page displays input data, control parameters, and a graphical representation of the
search area's map, enabling comprehensive management of the mine detection process.
The robot is responsible for landmine detection, autonomous navigation, and data transmission
at the project's core. The robot receives GPS coordinates from the server, stores search zone data and
passively detects anti-personnel mines while navigating the search area. As it identifies mines, it
updates the stored data and sends real-time information to the server, ensuring that the map and
relevant data remain current.
Web servers facilitate communication between the hardware and users, handling tasks such as
data storage, parameter calculations, and input/output operations. The initial data, entered by users
through the interface, include GPS coordinates and the search area boundaries, which are sent to the
servers for processing. Once the server's cloud functions are triggered, they calculate the search
area's boundaries and parameters, which are then transmitted to the robot. These values are stored
on the server and provided to the robot over a network connection.
Upon receiving the search parameters, the robot creates a data structure to track its path, detect
mine locations, and determine the boundaries of the search area. It first retains this data locally and
then periodically sends updates to the server via HTTP requests. These updates are stored on the
server and accessible to the web application. As the data is processed, a virtual map displaying the
mine locations and search progress is rendered in the user interface, providing users with visual and
informative real-time data (Figure 4). This system architecture allows users to remotely monitor and
control the mine detection process, facilitating more efficient and safer mine clearance operations
[7]. The software developed for this project incorporates a diverse array of advanced tools and
technologies:
Python is the primary programming language for developing machine learning models, data
preprocessing, and implementing auxiliary algorithms and methodologies.
MATLAB is employed for comprehensive data analysis and signal processing throughout the
project.
C++ is utilised to establish hardware interfaces and manage the operation of the mine
detection system.
HTML/CSS/JavaScript is used to build the project's web-based user interface.
Figure 4: Data Flow in the Landmine Detector Project
The HOMARD project represents a cutting-edge research initiative to create an advanced system
for detecting anti-personnel mines. This system integrates state-of-the-art robotics with
sophisticated machine learning techniques, leveraging a combination of ground-penetrating radar
sensors and advanced algorithms to detect landmines and other buried objects.
Within the HOMARD framework, various programming languages and tools are employed to
address different aspects of the system. Machine learning algorithms are primarily implemented in
Python, utilising well-known libraries such as TensorFlow and Keras. The robot control software
and data collection system are developed through C++ and Python, ensuring efficient hardware-
software integration.
To enhance detection accuracy, researchers in the HOMARD project have experimented with
various machine learning models, including Convolutional Neural Networks (CNNs) and Support
Vector Machines (SVMs). While specific data on the operational performance and accuracy of these
models has not yet been publicly released, the system is described by its developers as "promising,"
with expectations of significant contributions to mine detection technologies [8].
Table 2
Comparative Analysis of the Developed Mine Detection and Classification System
Characteristics Projects
Developed HOMARD Landmine Faster R- Hybrid
System Detector CNN model
Functionality Average Average High Low Low
Usability Average Average High Average Low
Reliability High - High Average Average
Performance Average - High High Average
Utilises Python Yes Yes Yes - -
Utilises C++ No Yes Yes - -
Employs Cloud Yes - Yes No No
Technologies
Integrates Robots or No Yes Yes Yes No
Drones (UAVs)
Accuracy of the Machine 99.2% - - 98.2% 99.3%
Learning Model
This section provides an analysis of research from publicly available sources, as well as
commercial projects. Several systems have been identified that use machine learning tools to detect
mines. Among them are both commercial projects and research for creating such projects. As a result
of the analysis, a table was formed, according to which it is possible to highlight the following trends
in the creation and research of mine detection systems [9-12]:
use of neural networks for accurate detection of mines such as convolutional, fully connected
and their modifications;
machine learning algorithms use data from sensors mounted on drones or robots to increase
mine detection safety;
systems additionally use a remote server for data collection, which has a positive effect on
the speed of data processing;
typical means of implementing machine learning algorithms are the Python and C++
programming languages.
3. Methods and means selection
3.1. Analysis of system functionality goals
The primary goal of the developed system is the high-precision detection and classification of
landmines. A neural network model will be employed to achieve this, with continuous improvements
driven by training on large datasets [12-18]. The system will offer remote access through a graphical
user interface (GUI) and integrated sensors. Additionally, the system must be hosted on a cloud
service to optimise performance. Objectives:
Objective 1 is to ensure High Detection Accuracy of Mines. The system must achieve a high
classification accuracy, measured by the Accuracy metric for the neural network model. It
will be accomplished by designing and training a robust model with extensive and diverse
datasets.
Objective 2 is to provide a Remote User Interface. The system should allow users to interact
with the software remotely without requiring direct sensor connections. Users will input data
via the interface to receive classification results, ensuring ease of use and flexibility.
Objective 3 is to develop a Graphical User Interface. The GUI will bridge the software and
the sensors, displaying critical data in a user-friendly format, including changes in magnetic
field anomalies and proximity to buried objects.
Objective 4 is to facilitate Data Addition and Updating. Continuous improvement of the
neural network model requires automatically expanding and updating the dataset. The
software must support remote access to a cloud service for storing and managing classified
data.
Objective 5 is to Enable Interaction with the Neural Network Model. A user-friendly interface
is necessary for interacting with the neural network, allowing users to train the model from
scratch, continue its training, create network copies, and save modifications. These features
will empower users to enhance the model's capabilities over time.
Objective 6 is to Ensure Data and Model Security is paramount for the integrity of the dataset
and the model. The system must implement robust usage restrictions, preventing
unauthorised alterations to the neural network's architecture or the stored data.
The following risks must be anticipated:
False Classification of Mines. The neural network may incorrectly classify objects as mines or
non-mines, impacting the system's reliability.
Loss of Connection to the Remote Server. Operators working in areas with limited connectivity
may experience difficulty accessing the remote server. In such cases, it is recommended that
sensors with local storage capabilities be employed.
Non-Operational Remote Server. Server failure could disrupt system functionality. To mitigate
this risk, the software should be hosted on multiple servers from different providers.
Interference with Database and System Code. Data integrity may be compromised due to
malicious actions or transmission errors. Safeguards must be implemented to protect against
unauthorised access and system corruption.
As a result of identifying the system's objectives and risks, a detailed set of requirements has been
established, summarised in Table 3 [18-27].
Table 3
Formation of Requirements
Type of Business User Functional Non-Functional
Requirement Requirements Requirements Requirements Requirements
Purpose They define the They define the Description of Description of how
tasks and objectives of the what the system the system being
actions of users business structure being developed developed in the
that the startup that the startup in the innovative innovative startup
will support will achieve and startup should do should operate to
the problems it perform its
will solve. functions.
Content of Soldiers will Users of the The system can Anyone can use the
the have the ability system are detect and system. For
Requirement to effectively individuals who classify successful
and safely neutralise landmines classification, users
detect explosive devices. remotely and must input specific
landmines. The system using sensors data at given
Project enables users to with specialised intervals. The
boundaries: perform detection software. system's quality
mined areas and classification attributes are
worldwide. tasks for accuracy, speed, and
Effects: timely, landmines. safety.
secure, high- The effect of task
precision. execution is
providing a safer
area post-
demining
compared to
conventional
methods.
3.2. Modelling System Requirements
The modelling of requirements for the passive detection and classification system for landmines will
be conducted using a use case diagram. Based on the defined objectives for system development,
three primary actors have been identified: the soldier, the application, and the developer. The soldier
is the leading actor in the system. The outcome of the requirement modelling process is creating a
use case diagram that reflects both functional and non-functional requirements, as well as the
interactions between the actors and the system. As an actor in the system, the soldier seeks to obtain
information regarding the presence of a landmine in a specific area of land. The prerequisites for this
use case include successfully installing the software on the sensor, remote access to the user
interface, the sensors' operational status, and network access [27-34]. If the software is connected
remotely, the reliability of the cloud service is also required.
The use case diagram (Figure 5) illustrates the following critical actions of the soldier:
Connect to the remote server.
Input data from the sensor into the remote server.
Receive classification results.
Figure 5: Use case diagram for the Soldier actor
Figure 6: Use Case Diagram for the Application Actor
The soldier will receive a definitive result regarding the type of mine if the remote interface is
utilised. Suppose the user employs a sensor already connected to the software. In that case, additional
information will be displayed, including the distance to the ground surface and a graph depicting the
changes in depth readings and magnetic field anomalies.
The software in the developed system will perform specific actions autonomously. Therefore,
creating an actor that reflects this functionality, namely the Application, is appropriate. This actor
represents the back-end component of the project, which will be hosted on a cloud service. The
prerequisites for this use case include the successful deployment of the project and the database on
the cloud service. The diagram (Figure 6) illustrates the following main tasks of the Application:
Data storage involves receiving and storing data that has been successfully classified in real-
time.
Mine classification occurs when a request is made by the soldier remotely.
Data submission to the database.
It updates the neural network model if installed on the local device.
The software, as well as the neural network model, requires technical support following
successful deployment. In this case, the actor of the Programmer is necessary (Figure 7). The model
should not be updated automatically, as this could decrease accuracy, necessitating a rollback to a
previous version. The responsibilities of the Programmer include analysing the obtained data,
training and retraining the neural network, updating the software, and interacting with the cloud
service.
Figure 7: Use Case Diagram for the Programmer Actor
3.3. Modelling system objects
The modelling of system objects will be executed through the utilisation of class diagrams (Figure
8). Most of the described methods align with the use case diagram while incorporating methods for
intermediate calculations. In addition to the previously identified actors, supplementary classes such
as Interface, Dataset, and Data will be introduced to augment functionality. The relationships
between these classes are delineated as follows:
Soldier-Interface: an association link, signifying that the soldier employs the interface to
classify mines;
Application-Interface: an association link illustrating that the application for its operational
functionality leverages the interface;
Application-Dataset: a composition link of one-to-one, indicating that the dataset constitutes
an integral component of the application;
Dataset-Data: an aggregation link of one-to-many, demonstrating that data is an essential
part of a singular dataset;
Application-Programmer: an association link, as the programmer influences and possesses
the capability to modify the application.
The soldier class represents the corresponding actor from the use case diagram. It includes
attributes such as an identifier and a name, which are essential for storing the user in the database.
The class also implements all use case scenarios and additional intermediary functions such as
retrieving distance, obtaining the mine classification, and generating graphs. A more detailed
description of the methods and attributes can be found in Table 4.
The Class Interface is essential for establishing interaction between the soldier and the
application. This class lacks attributes; however, it encompasses functions such as displaying
analysis results, receiving input data, and visualising the type of mine, distance, and changing
indicator graphs. A more detailed description of the methods and attributes is provided in Table 5.
Figure 8: Class Diagram
Table 4
Description of the Soldier Class Objects
Classes of Objects Class Attributes Class Methods
Class Purpose of Attribute Attribute Method Name Action Content
Name the Class Name Content
Soldier The main id Identifiers it get_analysis Receive classification
actor of the in the _info and additional
system database information about the
ground
name Soldier's full get_distance Display the distance
name from the sensor to the
ground
get_mine_type Display mine type
get_graph Display distance and
anomaly changes as a
curve
input_data Enter required data for
further classification.
establish Establish a connection
_connection with a remote server
The Application Class represents the corresponding actor within the system. It encompasses
attributes such as an identifier, name, and code for its storage in the database. The application
implements various methods, including conducting data analysis, classifying mines, calculating the
distance from the sensor to the surface, retrieving input data, storing data, and updating software on
the local device. A more detailed description of the methods and attributes can be found in Table 6.
Table 5
Description of the Interface Class Objects
Object Classes Class Attributes Class Methods
Class Class Attribute Attribute Method Name Action Content
Name Purpose Name Content
Interface API display_analysis Display all
between _info information after
Soldier successful
and classification
Application get_information Receive input data
from soldiers.
display_mine_type Display mine type
display_graph Display distance and
anomaly changes as a
curve
display_distance Display the distance
from the sensor to the
ground.
Table 6
Description of the Application Class Objects
Object Classes Class Attributes Class Methods
Class Class Attribute Attribute Method Action Content
Name Purpose Name Content Name
Application Responsible id Identifies analyse_ Perform analysis of
for access to objects ground the ground, start the
neural in the classification process
network database
model and name Application classify_ Perform mine
manipulation name mine classification based on
of data input data
code Unique calculate_ Calculate the distance
application code distance between the sensor
within the and the ground
system get_data Receive input data
save_data Save data to the
database.
update_ Update software in
software the local sensor
The Dataset class represents a data collection essential for training the neural network model. Its
primary purpose is to facilitate data manipulation. As such, the class does not possess any attributes;
however, it includes the following functions: save object, delete object, and retrieve object. A more
detailed description of the methods and attributes can be found in Table 7. The Data class represents
information about the mines with which the neural network model directly interacts. While the class
does not include any methods, it encompasses the following attributes: identifier, voltage, distance,
soil type, and mine type. A more detailed description of the methods and attributes can be found in
Table 8. The Programmer class is responsible for representing the corresponding actor in the system.
This class includes the following attributes: identifier and name. Additionally, it contains the
following methods: retrieve application data, update the neural network model, and update the
software. A more detailed description of the methods and attributes can be found in Table 9.
Table 7
Description of the Dataset Class Objects
Object Classes Class Attributes Class Methods
Class Class Attribute Attribute Method Action Content
Name Purpose Name Content Name
Dataset Responsible save_ Save the object to the
for manipulation object database.
of data needed delete_ Delete the object from
for neural object the database.
network model get_ Get an object from the
object database.
Table 8
Frequency of Special Characters
Object Classes Class Attributes Class Methods
Class Class Attribute Attribute Content Method Action
Name Purpose Name Name Content
Dataset Responsible id Identifies objects in the
for database
manipulation ground_ Represents one of the ground
of data type types
needed for voltage Display magnetic anomaly
neural heigh Displays the distance
network between a ground and a
model sensor
mine_ Represents correct mine type
type for an object
Table 9
Description of the Programmer Class Objects
Object Classes Class Attributes Class Methods
Class Class Attribute Attribute Method Action Content
Name Purpose Name Content Name
Program- Responsible id Identifies objects receive_data Get data from the
mer for in the database database and get
updating code from the remote
neural system
network name Represents the update_ Update neural
and name of the neural_ network model after
application programmer network additional training
update_ Update application
application
3.4. Modelling system processes
The modelling of system processes will be conducted using activity and sequence diagrams. These
diagrams will illustrate the primary successful scenario of how the user interacts with the software
product. The successful scenario represented in the activity diagram (Figure 9) comprises the
following sequence of steps:
Figure 9: Activity Diagram
1. Activating the Sensor
2. Awaiting User Input
3. If the application remains active, soil analysis is conducted. Otherwise, the application
terminates, and the user exits.
4. Concurrently, the system displays a graph illustrating variations in the magnetic field and
the distance to obstacles.
5. Mine Classification
6. If a mine is detected, the data is stored in the application, and the program returns to step 2.
If no mine is present, the program continues to operate from step 2.
The successful scenario illustrated in the sequence diagram (Figure 10) encompasses the following
sequence of steps:
1. Conducting Soil Analysis
2. Displaying the Magnetic Field Variation Graph
3. Calculating the Distance to Obstacles
4. Classifying the Mine
5. Presenting the Classification Results
6. Storing Classification Data
7. Displaying the Soil Analysis Results
Figure 10: Sequence Diagram
Figure 11: Component Diagram
The component diagram (Figure 11) illustrates two primary components: the software and the
cloud service. The software encompasses elements such as the soil analysis service and the neural
network for mine classification. Access to the service is facilitated through a user interface and
sensor integration, while interaction with the neural network occurs via an API. The cloud service
operates independently of the software and is designed for remote data storage. Access to the service
is conducted through console commands.
The operational objectives of the system have been meticulously articulated, encompassing the
identification of principal users and a comprehensive delineation of functional goals. The
requirements have been systematically formulated and modelled using a use case diagram. Object
modelling has been executed via class diagrams, which offer an in-depth exposition of the class
structures. Furthermore, the modelling of system processes has been successfully carried out, with a
particular emphasis on the primary successful use case scenario, employing both activity and
sequence diagrams.
This section describes the purpose of the system's operation, the primary users, and the
operation's objectives in detail. The requirements were formulated and modelled using a usage
diagram. Object modelling was done using a class diagram and described in detail using class
diagrams. Successfully modelled system processes, namely the main successful use case, using
activity and sequence diagrams.
4. Development of the project solution
4.1. Problem formulation and justification
The primary objective of this research is to devise a comprehensive conceptual framework for a
software system aimed at the passive detection and classification of landmines. The system's core
functionality categorises landmines by applying a deep artificial neural network, which delineates
classifications into five distinct categories.
The focus of this investigation is the classification process itself. The subject matter encompasses
the methodologies and tools utilised in developing a landmine classification system, particularly
within combat operations or demining efforts conducted by civil defence agencies. Specifically, this
study explores the classification process by implementing a deep artificial neural network featuring
configurations of one and two hidden layers and convolutional neural networks. Before the
classification process, it is essential to address the challenge of generating additional values within
the dataset, given that the current sample size is limited to 45 (representing the magnetic anomaly
values at depths ranging from 26 to 34 centimetres for the "Dry and Humus" soil type associated with
each of the five mine classifications). However, neural networks require training datasets that
encompass several thousand instances. To tackle this subproblem effectively, the proposed approach
employs a normal distribution function to facilitate data augmentation.
4.2. 3.2. Model construction for problem resolution
To illustrate the advantages of a deep neural network architecture, an alternative neural network
with a single hidden layer has also been developed [33-35]. This model consists of an input layer
comprising three nodes, followed by a first hidden layer containing seven nodes that utilise the ReLU
activation function. The output layer consists of five nodes that employ the Softmax activation
function. The Adam optimiser has been used with categorical cross-entropy as the loss function,
while accuracy is the principal performance metric (Figure 12). The output of this neural network is
quantitatively expressed by the relationship delineated in equation (1).
𝑦 =𝑓 (∑ 𝑤 𝑓 (∑ 𝑤 𝑥 )), 𝑖 ∈ {1; 5}, (1)
where 𝑦 – represents the element of the output vector of probabilities corresponding to the
association of the object with each class of mines, while 𝑓 – denotes the Softmax activation
function, 𝑤 – refers to an element of the weight matrix between the first and second hidden layers
and 𝑤 – represents an element of the weight matrix connecting the input layer to the first hidden
layer. Additionally, 𝑥 – signifies an aspect of the input feature vector associated with the mine [33-
35].
Figure 12: Architecture of the Neural Network Featuring a Single Hidden Layer
Figure 13: Architecture of the Neural Network with Two Hidden Layers
The neural network architecture with two hidden layers features an input layer size of 3, with
the first layer comprising seven neurons and employing the ReLU activation function. The second
hidden layer possesses identical characteristics to the first, while the output layer consists of 5
neurons utilising the Softmax activation function. The Adam optimiser is employed, with categorical
cross-entropy as the loss function, and accuracy is utilised as the performance metric (Figure 13).
The output of this neural network will be described by the equation (2).
𝑦 =𝑓 ∑ 𝑤 𝑓 ∑ 𝑤 𝑓 ∑ 𝑤 𝑥 , 𝑖 ∈ {1; 5}, (2)
where 𝑦 – represents an element of the output probability vector, indicating the relationship of
the object to each class of mines; 𝑓 – denotes the Softmax activation function; 𝑓 – refers to
the ReLU activation function; 𝑤 – is an element of the weight matrix connecting the second hidden
layer to the output layer; 𝑤 – represents an element of the weight matrix between the first and
second hidden layers; 𝑤 – is an aspect of the weight matrix linking the input layer to the first
hidden layer; 𝑥 – signifies an aspect of the input vector representing the characteristics of the mine
[33-35].
4.3. Selection and justification of problem-solving methods
In alignment with the established objectives, ensuring high accuracy in mine detection is imperative.
The primary challenge to be addressed is classification. In machine learning, classification pertains
to training a model to categorise or classify input data into predefined classes or categories. It
involves learning the decision boundary or mapping function that correlates the input features to
the corresponding output labels. The goal is to accurately predict the class of unseen instances based
on the patterns and relationships derived from the training data [36]. Several classification models
are discussed below.
Logistic Regression is a simple and widely utilised classifier that models the probability of
membership in a specific class based on input features. It estimates coefficients for each
feature and applies a logistic function for prediction. Logistic Regression is typically
employed for binary classification tasks or scenarios where the outcome variable is
categorical.
Bayes Classifier is grounded in Bayes' theorem, operating under the assumption of
independence among features. It computes the posterior probability for each class and selects
the class with the highest probability. The Bayes classifier is often applied in text
classification, spam filtering, and other high-dimensional data tasks.
Decision Tree models construct a tree-like structure by recursively partitioning data based
on feature values. Each internal node represents a feature test, while each leaf node
corresponds to a class label. Decision trees are beneficial for classification and regression
tasks and are recognised for their interpretability and ability to handle numerical and
categorical data.
The Random Forest method integrates multiple decision trees. Each tree is trained on a
random subset of the data, and the final prediction is determined by majority voting or
averaging the predictions from individual trees. Random forests are robust and efficient for
classification and Regression, often employed in tasks with complex datasets.
The Support Vector Machine (SVM) Classifier identifies the optimal hyperplane that
separates classes by maximising the margin between them. It maps data into a higher-
dimensional space to find a linear or nonlinear decision boundary. SVMs are commonly
utilised for binary classification tasks but can be extended to multiclass problems. They are
effective in high-dimensional spaces and can handle linear and nonlinear relationships.
Neural Network models are structurally and functionally akin to biological neurons.
Comprising interconnected layers of artificial neurons, neural networks learn from data
through forward and backward signal propagation. They excel in tasks involving large
datasets, complex patterns, and nonlinear relationships, finding widespread applications in
image classification, natural language processing, and various other fields [37].
At this juncture, the objective of providing a graphical user interface will not be implemented, as
it entails the development of a user sensor capable of detecting anomalies in the Earth's magnetic
field. Functions stipulated by other objectives may be realised within the back-end portion of the
system using cloud services.
4.4. Development of problem-solving algorithms
The generation based on the normal distribution entails the application of the standard distribution
formula [38] and the generation of pseudorandom values [39].
( ) (3)
𝑝(𝑉) = 𝑒 ,
𝑉 ∗ = 𝑟𝑎𝑛𝑑(𝑉 , 𝜎з ), (4)
where 𝑉 – represents the magnetic field anomaly value at a depth 𝐻 and soil type 𝑆 ; 𝑉 – denotes
∗
the new magnetic field anomaly value at a depth 𝐻 and soil type 𝑆 ; 𝑟𝑎𝑛𝑑(𝑉 , 𝜎з ) is a function that
generates pseudorandom values based on the normal distribution function, where µ – is the
arithmetic mean and σ is the standard deviation [39].
Consequently, the new values will be generated closer to those in the training dataset,
approximating the arithmetic mean and considering the standard deviation of the Earth's magnetic
field anomaly. The standard deviation is computed as follows:
𝜎з = 𝑎𝑣𝑔(𝜎 ), 𝑖 ∈ {1; 15}, (5)
where 𝜎з – represents the standard deviation of the Earth's magnetic field anomaly; 𝜎 – denotes
the standard deviation of the magnetic field anomaly at a depth 𝐻 .
The index і – takes values from 1 to 15, as the study [6] indicated that this range corresponds to
the distances at which the intensity of the magnetic anomaly of the mine is not detected.
The next phase involves data preparation. The following steps have been established for data
preparation:
1. Normalise magnetic field anomaly data based on the mean value and standard deviation [40].
𝑉′ =
𝑉
, (6)
where 𝑉′ denotes the normalised value of the anomaly; 𝑉 represents the initial value of the
anomaly; 𝑉 signifies the mean value of the anomaly; 𝜎 indicates the standard deviation of the
anomaly.
2. Additionally, encoding is applied to the soil type values, as these data are recorded as
categorical variables.
The primary challenge to address is the classification of mines using a deep neural network. A
classifier functions to establish a correspondence for each pair of object characteristics and their
respective classes. In this study, the characteristics of the object (the mine) are represented by a
vector comprising three values , corresponding to the magnetic field anomaly value of the
mine and the Earth in volts, depth in centimetres, and soil type, respectively. The classes of objects
constitute a set of five values C = {0, 1, 2, 3, 4}, representing the types of mines: "no mine," "anti-tank
mine," "anti-personnel mine," "booby trap," and "M14." The classification task is framed as the
identification of a classifier that ensures the minimum norms in Euclidean space:
min ‖𝑓 − 𝑓 ‖, (7)
where: 𝑓 denotes the target classifier; 𝑓 represents the deep neural network [41].
The mapping operator is known solely for objects in a finite training sample:
Xm = {(x1, y1), …, ( xm, ym )}, (8)
where Xm is the set of elements in the training sample with a size of m, the objective is to construct
an algorithm capable of determining the membership of any object х X to the class у Y [41-42].
5. Experiments
5.1. Selection and justification of development tools
The central objective of the system is the classification task, with the primary focus of development
being the design and implementation of an accurate neural network model. Neural networks are
typically developed using programming languages or specialised libraries. Below is a summary of
the most widely employed programming languages in the realm of machine learning:
Python is a widely utilised language for machine learning and neural network applications.
It offers a robust ecosystem of libraries and frameworks such as TensorFlow, Keras, and
PyTorch, which provide high-level abstractions and optimised implementations for neural
networks. Python's simplicity and readability make it an ideal choice for novice and
experienced developers, enabling rapid prototyping and seamless learning. The extensive and
active Python community of data scientists and machine learning practitioners provides
many resources, tutorials, and sample codes. Despite its numerous advantages, Python can
perform slower than lower-level languages like C++, particularly in computationally
intensive operations involving large-scale neural networks or tasks requiring maximal
efficiency [43].
R is traditionally employed for statistical computing and data analysis. Still, it also features
several machine learning libraries, including TensorFlow, Keras, and MXNet, which facilitate
the creation and training of neural networks. R's vast array of data analysis libraries makes
it well-suited for statistical modelling and exploratory data analysis. Its data visualisation
capabilities, exemplified by packages such as ggplot2, are highly effective for visualising
neural network results. However, R's interpreted nature can lead to slower execution times
when compared to languages like Python or C++, which may affect the performance of large-
scale neural networks and other computationally intensive tasks. Moreover, R's automatic
memory management could result in inefficiencies when handling memory-intensive
processes [44].
MATLAB is a programming language widely recognised in scientific and engineering
domains. It offers toolkits like the Neural Network Toolbox and Deep Learning Toolbox,
which provide a comprehensive suite of functions and algorithms for designing and training
neural networks. MATLAB's interactive environment enables rapid prototyping,
visualisation, and experimentation, facilitating development. However, MATLAB is
commercial software that requires a paid license, rendering it less accessible compared to
open-source alternatives such as Python. Additionally, its proprietary nature may limit
flexibility and customizability relative to open-source languages [45].
C++ is a high-performance programming language frequently used for systems programming
and computationally demanding tasks. Libraries such as TensorFlow, Caffe, and Torch
provide C++ APIs that enable the construction and training of neural networks. C++ offers
superior performance to interpreted languages like Python, making it suitable for resource-
intensive computations or large-scale neural networks. Its explicit memory management
gives developers granular control over memory allocation and deallocation. However, C++
has a steeper learning curve and may be more challenging than higher-level languages like
Python. The development process in C++ can be more time-consuming owing to its low-level
nature and the necessity for manual memory management [46].
To achieve the additional goals of the system, it is necessary to develop the back-end component.
The back-end is created using frameworks from various programming languages. Below is a
description of the most commonly used programming languages for back-end development:
Python boasts a robust ecosystem of libraries and frameworks, making it well-suited for web
development, data analysis, and scientific computing. Python is often chosen for its ease of
use, rapid growth, and strong community support. It is widely used for server-side web
development, creating APIs, data processing, and scripting.
JavaScript is a versatile scripting language primarily used for front-end web development.
However, with the advent of Node.js, JavaScript can now also be employed as a server-side
language. Node.js enables JavaScript development on the server side, making it an excellent
choice for building scalable, high-performance web applications. JavaScript (Node.js) is
frequently used to create real-time applications, render server-side, and handle many
simultaneous connections.
Java is a robust object-oriented programming language known for its platform independence
and scalability. It offers a wide range of libraries and frameworks for developing enterprise-
level applications. Its extensive toolset, performance, and strong tool support make Java
popular for large-scale server-side systems. Java is commonly used for enterprise application
development, distributed systems, and server-side components.
Ruby is a dynamic object-oriented programming language with an elegant and readable
syntax. It emphasises simplicity and developer productivity and is known for its focus on
developer satisfaction. Ruby features a mature web framework called Ruby on Rails, which
promotes rapid application development and follows the convention-over-configuration
principle. Ruby is often used for web development, prototyping, and building scalable web
applications through the Ruby on Rails framework.
PHP is a server-side scripting language designed specifically for web development. It is
widely used for building dynamic websites and web applications. PHP has a large ecosystem
of frameworks, such as Laravel and Symfony, which provide powerful tools and libraries for
web development. PHP is commonly used for website development, content management
systems (CMS), and e-commerce platforms.
5.2. Description of the developed project tools
The development of project tools includes creating the back-end component, a neural network
model, and their deployment on a cloud service. During development, a neural network, an object
generator, a graphical representation of the neural network's performance, the back-end component,
and the necessary file for container deployment were created.
The software for the neural network consists of tools for dataset generation, data preprocessing,
and the neural network model itself. As previously mentioned, number generation was performed
using pseudorandom number generation methods available in Python libraries. Data preprocessing
was handled by Panda's library, which is designed to work with large datasets. The steps carried out
by this script were outlined earlier. The neural network models were developed using the
TensorFlow library. The final product will use only the model with two hidden layers.
The back-end follows the basic structure of the Django framework, adhering to the Model-View-
Template (MVT) architecture. All required functions were developed in the views file, while database
tables were defined in the models file. The graphical interface is integrated using the Django Rest
Framework. To implement the required functionality, the neural network model was added to the
back-end files. As a result, when a user submits a classification request, the model will be loaded and
used to classify the data.
In this section, the development tasks are formulated and substantiated. A mathematical model
of direct propagation neural networks with one and two hidden layers was created, and their
architecture was implemented. As a result of the analysis of development tools, the following basic
functionality was chosen:
The neural network will be implemented using Python, the programming language in
particular, the TensorFlow library.
The back-end part of the system will be implemented using the Python programming
language and the Django framework.
System deployment will be done using Docker software.
6. Results and discussion
6.1. System testing
The neural network's performance was rigorously assessed using key metrics, including Accuracy
and AUC, alongside a series of visualisations that effectively capture the system's training and testing
outcomes. The results of the neural network classification are illustrated through the following
diagrams (Figure 14-17):
Figure 14: Confusion Matrix, where a – Neural network with one hidden layer, b – Neural network
with two hidden layers
Figure 15: ROC Curves for Each Type of Mine, where a – Neural network with one hidden layer, b
– Neural network with two hidden layers.
1. Confusion Matrix Heatmap visualises the confusion matrix of the classification results,
where the X-axis corresponds to the predicted classes, and the Y-axis represents the actual
classes (Figure 14). For clarity in presenting large values, scientific notation (e.g., "e+02") is
employed, indicating that the given number should be multiplied by 10². The heatmap reveals
a high classification accuracy, with most classes correctly classified. More than 180 samples
per class were accurately predicted, while the total number of misclassified samples remains
below 10, underscoring the model's robust performance [47].
Figure 16: Precision-Recall Curve, where a – Neural network with one hidden layer, b – Neural
network with two hidden layers.
2. The Receiver Operating Characteristic (ROC) curve provides a detailed assessment of
the classification quality across each class. The X-axis tracks the growth in actual positive
classifications, while the Y-axis measures the increase in false positive classifications (Figure
15) [48]. This visualisation enables a precise evaluation of the model's discriminatory power
for individual classes, highlighting its effectiveness in distinguishing between them.
3. Precision-Recall curves offer additional insight into the model's classification performance
and incremental improvements. The variation in recall is represented on the X-axis, while
the Y-axis displays the corresponding changes in Precision (Figure 16). Additionally, the F-
score, a harmonic mean of Precision (P) and Recall (R) [49], is depicted, serving as a
comprehensive measure of the model's overall performance. The F-score is calculated as
follows:
𝐹= . (9)
4. The graph includes iso-F1 curves, representing lines where the values of precision and recall
yield a corresponding F1 score [50]. These curves provide an intuitive understanding of the
balance between precision and recall and how this affects the F1 criterion.
5. Accuracy and Loss Curves illustrate the accuracy metric and loss function after each
training epoch. The values of accuracy and loss are shown on the Y-axis, while the X-axis
represents the progression of training epochs (Figure 17) [51].
Figure 17: Accuracy-Loss Curves, where a – Neural network with one hidden layer, b – Neural
network with two hidden layers.
The ROC curves for the two-hidden-layer model exhibit superior performance compared to a
similar neural network with a single hidden layer. The area under the curves (AUC) in Figure 15 is
larger across all mine classes. An AUC value exceeding 0.99 was achieved, demonstrating exceptional
classification performance for each mine class. It indicates that the neural network model reliably
distinguishes between different types of mines with remarkable precision.
Figure 18: The relationship between accuracy and loss metrics relative to the number of neurons in
the first (a) and second (b) hidden layers.
Table 10
Results of Neural Networks by Accuracy Metric Relative to Optimizers
Optimisers 1-layer NN 2-layer NN
Adam 0.9790 0.9923
RMSprop 0.9790 0.9856
SGD 0.9695 0.9812
Table 11
Results of Neural Networks by Accuracy and AUC Score Metrics
Metrics 1-layer NN 2-layer NN
Accuracy 0.9790 0.9923
AUC score 0.9870 0.9953
Figure 19: Correlation between accuracy and loss metrics as influenced by the activation function
in the first (a) and second (b) hidden layers, each comprising seven neurons
The heatmap demonstrates that the number of misclassified classes is significantly reduced, with
over 180 correctly classified samples for each class and fewer than ten misclassifications overall.
Training a neural network with two hidden layers accelerates learning, as shown in Figure 16.
Consequently, the model is capable of achieving higher accuracy at a faster rate compared to a neural
network with only one hidden layer.
The F1-score is consistently high, exceeding 0.8 (Figure 16), which indicates a low number of false
negatives and false positives, thus reflecting the model's accuracy in classification.
Figure 20: Correlation between accuracy and loss metrics on the activation function in the output
layer
The loss values for the neural network are impressively low, falling below 0.1, in contrast to
similar studies where loss values peaked at 0.1. It suggests that the neural network training process
is highly efficient, resulting in better overall performance and convergence.
Experiments were conducted on neural networks with one and two hidden layers. The networks'
effectiveness was evaluated using the metrics of Accuracy and AUC score. The optimisers Adam,
RMSprop, and SGD were selected for optimal performance. The assessment of these optimisers was
carried out using the Accuracy metric. The results are presented in Tables 10 and 11. To enhance the
performance of the neural network, a comprehensive investigation into its symmetry was
undertaken. Symmetry within the neural network architecture is defined by an equal distribution of
neurons across all layers [52] and the uniformity of activation functions [53] concerning the
established symmetry plane. In this study, the plane of symmetry was delineated between the first
and second hidden layers.
The following experimental procedures were implemented:
1. Variation of Neuron Count: Systematically altering the number of neurons in the first and
second hidden layers, ranging from 3 to 49.
2. Modification of Activation Functions: Adjusting the activation functions employed in the
first and second hidden layers.
3. Assessment of Output Layer Performance: Evaluating the performance outcomes of the
model upon varying the activation function within the output layer.
Figure 18 elucidates the results, wherein the orange line denotes the accuracy metric, while the
blue line represents the loss function. The Y-axis encapsulates the metrics of accuracy and loss,
whereas the X-axis indicates the neuron count in both the first and second hidden layers.
The findings indicate that asymmetry between the first and second hidden layers exerts negligible
influence on the model's accuracy (≤ 1%); however, a significant escalation of up to 75% in the loss
function occurs when symmetry is compromised. Notably, the choice of activation functions within
the output layer of the neural network yields profound implications for performance. Specifically,
substituting the ReLU function in the output layer with alternatives such as softmax, softplus,
sigmoid, or exponential functions results in a remarkable increase in accuracy, rising from 21% to an
impressive 98%. In stark contrast, the loss function attains its apex (exceeding 6) when utilising
activation functions such as ReLU, SELU, ELU, and tanh. In contrast, it exhibits minimal values when
employing softplus, softmax, sigmoid, and exponential functions.
The observed metrics for accuracy and loss can be elucidated through gradient vanishing and
explosion. In the case of the ReLU function, as depicted in Figure 19, both vanishing and exploding
gradients are absent, given that the derivative of the ReLU function remains constant for positive
input values. Conversely, all other activation functions demonstrate vulnerability to the issues of
gradient vanishing and explosion, attributed to their variable derivatives within their respective
domains. Figure 20 further exemplifies this inverse relationship.
6.2. System deployment
The system's initial deployment will be orchestrated using Docker software, which will also
encompass the database within the container. The following steps elucidate the process of creating
and launching the Docker container:
The creation of the Dockerfile step entails the formulation of a specialised file titled
"Dockerfile" located in the application's root directory. This file articulates a comprehensive
sequence of instructions for initialising the application and the database within the container.
Specifically, it designates the official Python 3.9 image as the base image, establishes the root
directory as /app, copies the requirements.txt file into the container, installs the requisite
dependencies, transfers the application code to the container, opens port 8000, and initiates
the server.
The creation of the requirements.txt File process involves generating a dedicated file that
enumerates all essential packages and libraries, along with their respective versions, which
are imperative for the successful execution of the application within the container.
The Docker Image command from the application's root directory is executed in the terminal.
Launching the Docker Container e container must be instantiated using a specific command.
Upon successful initiation, the container will be visible in the Docker registry, with the back-
end and database fully operational [52].
The procedures for testing and deploying the system have been meticulously documented. Each
component of the system was subjected to various testing methodologies, with the neural network's
performance represented through corresponding metrics and visualisations.
The system deployment was conducted locally utilising Docker software, incorporating both the
database and back-end components. For future development endeavours, it is advisable to consider
procuring hardware from cloud service providers.
7. Conclusions
This research has presented a sophisticated system for the passive detection and classification of
mines utilising deep neural networks. Two models were developed, featuring one and two hidden
layers, which achieved accuracies of 97.9% and 99.2%, respectively. Although the neural network
with a single hidden layer exhibited slightly lower accuracy than its counterpart with two hidden
layers, it was less computationally intensive during the training phase. Remarkably, the classification
accuracy attained in this study was 99.2%, surpassing the performance of a similar heuristic
algorithm, k-NN, which employed a fuzzy metric by a margin of 1%.
In comparison to the findings presented in reference [6], several notable advancements were
achieved, including superior accuracy metrics:
1. Heatmap Analysis results illustrated in the heatmap demonstrated a significantly reduced
number of misclassified instances, with correct classifications exceeding 180 for each
category. At the same time, the total misclassifications were limited to fewer than 10.
2. The learning process of a neural network with two hidden layers is faster, as shown in Figures
16-18. Thus, the training and accuracy of a model with two hidden layers is faster than that
of a neural network with one hidden layer due to the optimised morphology of the neural
network with two hidden layers.
3. ROC Curve Performance values were notably higher than those reported in the analogous
study involving a single hidden layer. The graphs in Figure 15 exhibited larger areas under
the curves across all mine classes, indicating enhanced discriminative capability.
4. An AUC of more than 0.99 was achieved, showing high efficiency in classifying each mine
class. The neural network model clearly distinguishes one type of mine from another, taking
into account the soil structure.
5. The F1 metric takes a value greater than 0.8, which means a low number of classified false
negatives and false positives.
6. The loss rates for the neural network reach a value of less than 0.1, compared to the rates
from another study where they peaked at 0.1, which means practical neural network training.
The described approach is not perfect and could be improved. To improve the performance of passive
mine detection based on data from magnetic field sensors and their classification using neural
networks, the following steps must be taken:
Increase the dataset based on actual data, considering different types of soil structure.
Develop optimised models of other neural network architectures.
Acknowledgement
The research was carried out with the grant support of the National Research Fund of Ukraine
"Methods and means of active and passive recognition of mines based on deep neural
networks", project registration number 273/0024 from 1/08/2024 (2023.04/0024). Also, we would like
to thank the reviewers for their precise and concise recommendations that improved the
presentation of the results obtained.
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