=Paper=
{{Paper
|id=Vol-2300/Paper16
|storemode=property
|title=Two-Dimensional Spectral Detector for Baggage Inspection X-Ray System
|pdfUrl=https://ceur-ws.org/Vol-2300/Paper16.pdf
|volume=Vol-2300
|authors=Mykola Shutko,Volodymyr Shutko,Lidiya Tereshchenko,Maksym Zaliskyi,Iuliia Silantieva
|dblpUrl=https://dblp.org/rec/conf/acit4/ShutkoSTZS18
}}
==Two-Dimensional Spectral Detector for Baggage Inspection X-Ray System==
https://ceur-ws.org/Vol-2300/Paper16.pdf
63
Two-Dimensional Spectral Detector for Baggage
Inspection X-Ray System
Mykola Shutko1, Volodymyr Shutko2, Lidiya Tereshchenko3, Maksym Zaliskyi4, Iuliia
Silantieva5
1. Information Security Devices Department, National Aviation University, UKRAINE, Kyiv, 1 Komarova av., email:
Lenochka597@ukr.net
2. Electronics Department, National Aviation University, UKRAINE, Kyiv, 1 Komarova av., email: vnshutko@ukr.net
3. Aviation Radioelectronic Complexes Department, National Aviation University, UKRAINE, Kyiv, 1 Komarova av., email:
10118@ukr.net
4. Aviation Radioelectronic Complexes Department, National Aviation University, UKRAINE, Kyiv, 1 Komarova av., email:
maximus2812@ukr.net
5. International Transportation and Customs Control Department, National Transport University, UKRAINE, Kyiv, 1, M. Omelianovycha-
Pavlenka Str., email: gmelanine@gmail.com
Abstract: The paper deals with two-dimensional II. LITERATURE REVIEW AND PROBLEM STATEMENT
spectral detector for baggage inspection X-ray devices.
This detector is based on construction of analytical The paper addresses applied research challenges
models for internal structure of object under control and concerning development and application of a new method of
their spectrum calculation. The methods of projective determination (visualization) of the internal structure of the
geometry and Bouguer-Lambert law are applied to obtain objects under control (OC), that enables dangerous OC to be
the analytical models for shadows of the three- identified with high probability in real time, increases the
dimensional objects. Spectral detector are designed speed of dangerous substances identification in luggage, and
according to Neyman-Pearson criterion. Analysis shows provides automation of these processes. In addition,
that proposed spectral detector has good operating automatic generation of images of hazardous OC allows for
characteristics even at low signal-to-noise ratios. periodic inspections of aviation security service operators.
Keywords: aviation security service, X-ray, optical Detection systems based on X-ray, computer tomography
imaging, shadow of the three-dimensional objects, and spectroscopy of mobile ions have certain shortcomings
spectral detector. [1 – 7]. Some of these systems can detect well-hidden
explosives, but their implementation requires considerable
I. INTRODUCTION funds. In addition, they have a high level of false alarms
(approximately 0.2 ... 0.4).
Ensuring effective protection against terrorism is the most
difficult issue, especially for countries with a developed air Thus, the development of analytical models for the receipt
transport network, a large number of airlines and airports. of multidimensional shadows of translucent objects for
further processing will allow the classification of OC, which
The problem is complicated by unpredictability of terrorists’
will greatly facilitate the work of operators serving
actions. In addition, vulnerabilities in aviation security
supervision devices in Aviation Security Service (AvSS),
systems (such as procedures for screening airline passengers
reducing the value of false alarms.
and their baggage, freight shipments, mail, etc.) that can be
exploited by law violators should be taken into consideration. Literature analysis showed that modernization of
The main way to improve aviation safety is to prevent equipment for AvSS is carried out in two directions: in the
part of the improvement of hardware and software. In [8]
hazardous objects and substances, explosive devices and
authors proposed new X-ray backscatter technique using an
weapons on aircraft board. This requires a comprehensive
un-collimated powerful (high kW) X-ray beam and an
development and introduction of new methods of screening,
efficient pinhole camera encompassed with a high resolution
detection and identification of dangerous objects under
control. matrix detector for imaging of an object. Moreover, a high-
Insights of the direct visualization methods indicate that energy X-ray inspection technique for the reliable inspection
of air freight container was presented in [9].
they are inherent in the same type of operations: primary
Analysis of various strategies for object detection in X-ray
radiation exposure of the objects under control in
security imagery is given in [10]. Moreover, paper [11] also
configuration space (in the case of active method), reradiation
deals with a technique for the classification of X-ray baggage
reception (scattered or passed through the object), its
conversion into an electrical signal, signal processing and images using convolutional neural networks. Application of
deep convolutional neural network as classification method in
electrical-to-optical signal conversion.
medicine X-ray image analysis was considered in [12].
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
64
In [13] authors investigated the feasibility of applying Internal visualization of the OC with a complex form, in
straight-line-trajectory-based tomographic imaging this case a sphere in the sphere, designed with point source is
configurations to security inspections. The method of shown in the Fig. 3.
automated target recognition with usage of reference
database, which contains X-ray images of OC, for cargo
scanning systems was proposed in [14]. The papers [15, 16]
deal with procedures of handguns, shuriken and razor blades
recognition for baggage inspection.
The simulation of the internal structure for OC with simple
and complex forms using the point source of irradiation in the
center, as well as with the bias relative to the center, is
considered in [17]. The method developed for optical
imaging of the inner structure of the three-dimensional
objects allows obtaining a shadow of these objects, exposed
to electromagnetic radiation. It has useful applications in
different life spheres, as in medicine, manufacturing industry,
in a process of customs supervision of goods and means of Fig. 3. Inner structure imaging for OC
transport for commercial use, etc. It allows the AvSS to
The simulation shows that the simplest objects have
increase the probability of correct detection of hazardous
shadows with transient characteristics, half-dooms,
materials and reduce false alarms of its security system. For
distortions of the type of the crater, where there are generally
medicine the method may help to increase the probability of
flat irradiating planes. Changing the irradiation angle changes
health hazard anomaly detection.
the shadow to unrecognizability. To accurately identify the
So aim of this paper is synthesis of two-dimensional
intended OC, it is necessary to automate the process of
spectral detector for baggage inspection X-ray devices.
recognizing shadows, taking into account possible distances
III. TWO-DIMENSIONAL SPECTRAL DETECTOR between the source, the OC and the screen-receiver, the
irradiation angles, etc.
The construction of an analytical model reduces to the Methods of analytical modeling of the OC with different
calculation of a projective image of isotropic object in the shapes, geometrical dimension, foreshortening, substance and
case of homogeneous irradiation by a point source located on appropriate extinction coefficients, used to develop
the axis of object symmetry perpendicular to the plane of the procedures for identifying dangerous objects under security
image (screen). supervision of passengers and baggage, allow to image OC
To determine a position of the radiation source, the OC and inner structure.
the screen with a point source it is appropriate to use In order to verify the developed models multidimensional
cylindrical coordinate system applied to the Fig. 1. The OC spectra of visualization images are obtained.
model with complex form is presented in Fig. 2. Procedure for image processing consists of using a shadow
of the object of given shape to construct a two-dimensional
spectrum and its subsequent use in developing the standard
spectral detector proposed in the research. This detector is
invariant with respect to the location of the OC in the
working area.
The invariance of the calculated spectrum to the location of
the OC on the plane of the screen provides the possibility of
applying algorithms for the calculation of two-dimensional
spatial spectra of the visualization image in relation to the
wanted images of some image anomalies in the introscopic
imaging systems of the AvSS.
Fig. 1. OC scanning: (а) is the setting a cylindrical coordinate That is, the desired density distribution of the object of
system; (b) is the setting a scanning beam position control μ(x, y) must be matched to fit its two-dimensional
( )
spatial spectrum – Fourier-image M k x , k y . In the further
processing of visualization data, we find solutions in the
( )
frequency space M k x , k y , and then, through the inverse
Fourier transform, the desired distribution is calculated
µ * ( x, y ) . The resulting distribution is selected according to
those images, which are in the memory of the supervision
system. A decision is made to detect a particular object after
Fig. 2. OC with complex form matching the resulting image µ * ( x, y ) and mask µ * ( x, y ) .
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
65
Figures 4, 5 shows the spectra of images of different When using X-ray systems in order to provide automation
shades of opaque OC of a simple shape on size a 100x100 of care and increase the reliability of decision-making on the
screen plane located almost above the center of the screen. presence of prohibited articles and substances in the OC,
there are problems of identifying different forms and
locations of the OC.
For this purpose, on the example of spectral detector model
was constructed in the Matlab environment. In this case, the
detection occurs regardless of the OC location and regardless
of its shape and size.
The considered models are the shadows of two objects in a
field with specified boundaries. One object is a regular square
а) b)
(this kind of can have a dynamite), and the other is a model
of the machine gun (Fig. 8). Also, white Gaussian noise and a
Fig. 4. Shadow (a) of a parallelepiped and its spectrum (b) mixture of image and noise are modeled (Fig. 9). The
developed program allows us to detect an OC with a given
probability of false alarms for the corresponding threshold
decision depending on the size of the OC. The program
calculates the probability of correct detection of a signal from
an OC.
а) b)
Fig. 5. Shadow (a) of the cylinder and its spectrum (b)
On one plane, the shadows of two parallelepipeds are
located, and their spectral images are obtained (Fig. 6). a) b)
Fig. 8. Model of shadow OC
A mixture of useful signal and noise is shown in Fig. 9.
a) b) c)
Fig. 6. Shadows of two parallelepipeds and their spectrum:
a) shadows of two parallelepipeds; b) three-dimensional image of
the spectrum those shadows; c) a two-dimensional projection of the
a) b)
spectrum of shadows of parallelepipeds
Fig. 9. A mixture of signal with noise in cases of signal-to-noise
The following figures show the spectral images of the ratios equaled to 2 (a) and 0.5 (b)
shadows of the parallelepiped and the spheres that were
located in space (Fig. 7). The Neyman-Pearson criterion is applied for optimal
detection of an OC. According to the Neyman-Pearson
criterion, the threshold level V is determined from the
condition that the probability of a correct detection D with
the given probability of false alarm F was maximal. Hence,
the optimal character of the Neyman-Pearson criterion is that
it maximizes the probability of correct detection at a fixed
probability of false alarms.
a) b) c) In addition, it should be noted that the program calculates
Fig. 7. Shadows of parallelepiped and spheres and the spectrum of the characteristics of the detection. An example of these
their compatible shadows: a) shadows; b) three-dimensional image characteristics is shown in Fig. 10.
of the spectrum those shadows; c) a two-dimensional projection of On these graphs it is seen that when the decision threshold
the spectrum of shadows is reduced, the detection characteristic is more efficient,
Analysis of the spectra of hazardous and forbidden OC however, the probability of false detection is increased.
allows us to create an appropriate database for the further The analysis shows that the developed spectral detector has
detection of OC of various shapes and complexity. good detection characteristics even at low signal-to-noise
ratios.
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
66
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