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  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>A road sign inventory system based on radio-frequency identification *</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Vladimir Mavlyutov</string-name>
          <email>monahovaman@gmail.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Oleg Golovnin</string-name>
          <email>golovnin@ssau.ru</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Samara University</institution>
          ,
          <addr-line>Samara</addr-line>
          ,
          <country country="RU">Russia</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>Recently, the number of road signs on the streets has been increasing, and therefore there is a need to automate the inventory of road signs. This work presents a developed system for inventory of road signs and other technical means of traffic management. The system provides information acquisition using radio frequency identification (RFID) technology. To reduce the time spent on inventory of road signs, it is proposed to install an RFID tag reader on public transport. During the flight, the reader collects all the data from the read RFID tags, fixed on the road signs. The hardware, middleware and software for the inventory system were developed. The hardware is represented by the RFID Reader/Writer and RFID Tag. The software provides functions for accounting of road signs, resolving problems during the inventory and the formation of an inventory report. Middleware provides the interaction of software and hardware. In the work, a comparative analysis of several ways of inventorying road signs was carried out: using manual processing (field surveys), using a system based on video cameras, and using the developed system. As an efficiency criterion, we used the average time for an inventory of 1 km of road in urban (street) and suburban (highway) conditions, including the time to collect the initial data and enter records into the database. The time taken to inventory of 1 km of street in urban conditions was reduced by 53%, while in suburban conditions time was reduced by 69%. In conditions of the continuous reduction in price of passive RFID tags, the application of the proposed approach seems justified.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>Introduction</title>
      <p>
        Road signs are installed to increase the safety of vehicles and pedestrians. Therefore, it is necessary to take timely
measures in case of their absence or damage. Recently, the number of road signs should be not only their typical, but also
detailed characteristics [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ].
      </p>
      <p>
        An inventory of road signs can be performed by automated systems based on stereo vision [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ]. Such systems analyze
video streams from several cameras and, if a road sign is detected, record information about it and its geolocation
obtained using a GPS receiver or odometer. Basically, work in this area is aimed at improving the quality of recognition
of road signs [
        <xref ref-type="bibr" rid="ref3">3</xref>
        ], for example, at the expense of complex multi-stage approaches that take into account the characteristics
of roads [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ].
      </p>
      <p>
        Existing modern approaches well recognize well-known road signs, but cannot recognize complex signs and their
characteristics necessary for carrying out an inventory. In [
        <xref ref-type="bibr" rid="ref6 ref7">6, 7</xref>
        ], approaches using a convolutional neural network were
proposed, which make it possible to recognize complex road signs, but the question of obtaining the characteristics of
road signs is still not resolved.
      </p>
      <p>
        Most of the research in the field of artificial intelligence and neural networks is focused on the development of
technologies for recognizing road signs, and the issues of matching signs with their standard image are almost not
available [
        <xref ref-type="bibr" rid="ref8">8</xref>
        ]. Road signs are not standard, which can lead to misinterpretation by the driver and a traffic accident [
        <xref ref-type="bibr" rid="ref9">9</xref>
        ].
This is not only compliance with the actual position of the road sign on the road [
        <xref ref-type="bibr" rid="ref10">10</xref>
        ], but also compliance with standard
templates and other characteristics.
      </p>
      <p>
        A number of works are devoted to the analysis of publicly available Big Data, for example, based on the analysis of
photographs with geotags from Google Street View, it is possible to obtain sufficiently accurate information about the
signs and its location [
        <xref ref-type="bibr" rid="ref11 ref12">11, 12</xref>
        ], which can be used for inventory and updating databases.
      </p>
      <p>
        The development of “smart cities” makes it possible to use radio frequency communications (RFID) technologies in
solving a wide range of problems [
        <xref ref-type="bibr" rid="ref13 ref14">13, 14</xref>
        ]. In [
        <xref ref-type="bibr" rid="ref15">15</xref>
        ], theoretical aspects of radio wave propagation were considered and
experimental approbation of technology for inventory of road signs based on passive RFID tags was carried out.
      </p>
      <p>This work presents a developed inventory system that collects information about road signs and other technical means
of traffic management using RFID technology. The system provides both the collection of information from passive
RFID tags fixed to road signs and the updating of information in tags, if necessary.
2
2.1</p>
    </sec>
    <sec id="sec-2">
      <title>RFID-based Approach to Road Sign Inventory</title>
      <sec id="sec-2-1">
        <title>Approach Description</title>
        <p>The following approach to conducting an inventory based on RFID technologies is proposed (Figure 1).</p>
        <p>Inventory information about a road sign (or other technical means of traffic management) is recorded in a passive
RFID tag fixed on the back of the road sign. To reduce the time spent on inventory of road signs, it is proposed to install
an RFID tag reader on public transport. During the flight, the reader writes down all the data about the read marks
attached to the road signs. Public transport must pass in both directions, since modern RFID technologies provide a
reading radius of about 15 meters. At the end of the flight, the data is transmitted to a server that checks the data received
from the reader. In this case, collisions may occur, for example, if one of the labels does not appear repeatedly, this may
mean its mechanical damage, absence or inability to read due to the state of the environment. If the label does not appear
for several times, then this may mean the loss of a road sign.</p>
        <p>Figure 2 presents an algorithm that describes the functioning of the inventory system using the proposed approach.</p>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>System Components</title>
      <p>The hardware, middleware and software for the inventory system were developed. Hardware using RFID reader / writer
and RFID tags. The software provides functions for accounting for road signs, resolving problems during the inventory
and the formation of an inventory report. Middleware provides the interaction of software and hardware.
3.1</p>
      <sec id="sec-3-1">
        <title>RFID Reader/Writer</title>
        <p>An RFID reader / writer hardware circuit has been developed (Figure 3). A passive system of the UHF 860 ~ 960 MHz
band is used, standard EPC Class 1 Generation 2 (ISO / IEC 18000-63) – Gen2. A stationary reader was selected that
provides the maximum speed and range of registration due to the use of high-performance digital signal processors that
emit a weak tag response signal against the background of the carrier radio frequency, noise and interference.</p>
        <p>The developed device allows both reading data from the tag and writing it to it. Effective range – 15 meters, reading
speed – 400 tags per second.
3.2</p>
      </sec>
      <sec id="sec-3-2">
        <title>RFID Tag</title>
        <p>For the microcontroller, which is used in the RFID reader / writer, developed middleware in the C programming
language, which provides the following functions:
• writing and reading data to / from RFID tags;
• checking the operability of the device;
• collection of information about read tags (up to 13.5 thousand);
• transfer of an array of data to the server.
3.4</p>
      </sec>
      <sec id="sec-3-3">
        <title>Database</title>
        <p>PostgreSQL / PostGIS was chosen as the database management system, because it provides geo-data to describe the
location of road signs. The developed database structure of the system is shown in Figure 4.</p>
        <p>The database implements the Inventory Record Model – it distinguishes the parent class “Technical means of traffic
management”, from which the descendant classes: “Road Sign”, “Traffic Light”, “Barrier Fences” and others are
inherited.</p>
        <p>The database is used to store inventory records from read tags and is used in accounting software.
3.5</p>
      </sec>
      <sec id="sec-3-4">
        <title>Accounting Software</title>
        <p>Accounting software has been developed for the inventory system in C# in the Visual Studio 2017 programming
environment. The main user form is shown in Figure 5. A classic table view with the ability to sort and filter is
implemented.</p>
        <p>Accounting Software provides the following functions:
⎯ adding / deleting / editing entries in manual mode;
⎯ updates of information based on data received from RFID Reader / Writer;
⎯ conflict resolution;
⎯ formation and printing of an inventory report.
In the work, a comparative analysis of several ways of inventorying road signs was carried out: using manual processing
(field surveys), using a system based on video cameras and using the developed system based on RFID technology. As an
efficiency criterion, we used the average time for an inventory of 1 km of road in urban (street) and suburban (highway)
conditions, including the time to collect the initial data and enter records into the database.</p>
        <p>The studies were conducted in the urban district of Samara, Russia. As the urban street, “Prospekt Maslennikova” was
selected with a length of 1.67 km (Figure 6).
The section of the “Krasnoglinskoye Shosse” highway with a length of 5.42 km was selected as a highway (Figure 7).</p>
        <p>On a suburban highway, the number of road signs and the density of their installation are less than on an urban street.
Another significant difference between urban and suburban surveys is the speed of the automobile laboratory. On the
urban street, the average speed was 17 km per hour, on the suburban highway – 41 km per hour.</p>
        <p>
          Field surveys were carried out by 1 specialist surveyor, data were entered by 1 operator. As an inventory system
based on video cameras, the “WayMark” system [
          <xref ref-type="bibr" rid="ref16">16</xref>
          ] was used in combination with 2 cameras directed forward and to
the right by 45 degrees to the direction of movement. The developed system based on RFID technology used 1 receiver.
Thus, both road laboratories had to pass each road twice (in the forward and reverse directions) in order to fix signs on
both sides of the road. The calculation results are shown in table 1.
        </p>
        <p>Inventory system</p>
        <p>Field surveys
Camera-based system
RFID-based system</p>
        <p>Thus, the time taken to take an inventory of 1 km of street in urban conditions was reduced by 53%, while in
suburban areas time was reduced by 69% compared to the “WayMark” system.
5</p>
      </sec>
    </sec>
    <sec id="sec-4">
      <title>Conclusion</title>
      <p>Thus, there is presented an approach to inventory road signs and other transport infrastructure objects using RFID
technology. The approach is implemented as an automated inventory system containing a set of technical and hardware
tools. The inventory system implements the following functions:
• recording of inventory information in tags, which are fixed on the road signs;
• collecting information from tags fixed on road signs using RFID technology;
• decision making in case of conflict;
• maintaining a database of road signs and other technical means of traffic management;
• formation of an inventory report.</p>
      <p>Comparison of the effectiveness of the developed system with an inventory system based on video processing showed
that the time taken to inventory of 1 km of street in urban conditions was reduced by 53%, while in suburban conditions
time was reduced by 69%. It is also important to reduce the influence of the human factor, which helps minimize labor
costs and reduce risks. In conditions of the continuous reduction in price of passive RFID tags, the application of the
proposed approach seems justified. However, there are difficulties with the operation of this approach on multi-lane
highways, due to the limited range of the RFID receiver.</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          1.
          <string-name>
            <given-names>O.</given-names>
            <surname>Golovnin</surname>
          </string-name>
          ,
          <string-name>
            <given-names>T.</given-names>
            <surname>Mikheeva</surname>
          </string-name>
          .
          <article-title>Detailed Models and Network-Centric Technologies of Transport Process Management</article-title>
          .
          <source>5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems: 768{773</source>
          ,
          <year>2017</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          2.
          <string-name>
            <surname>K.C.P. Wang</surname>
            ,
            <given-names>Z.</given-names>
          </string-name>
          <string-name>
            <surname>Hou</surname>
            ,
            <given-names>W.</given-names>
          </string-name>
          <string-name>
            <surname>Gong</surname>
          </string-name>
          .
          <article-title>Automated road sign inventory system based on stereo vision and tracking</article-title>
          .
          <source>Computer‐Aided Civil and Infrastructure Engineering</source>
          ,
          <volume>25</volume>
          (
          <issue>6</issue>
          ):
          <volume>468</volume>
          {
          <fpage>477</fpage>
          ,
          <year>2010</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          3.
          <string-name>
            <given-names>S.B.</given-names>
            <surname>Wali</surname>
          </string-name>
          ,
          <string-name>
            <given-names>M.A.</given-names>
            <surname>Abdullah</surname>
          </string-name>
          ,
          <string-name>
            <given-names>M.A.</given-names>
            <surname>Hannan</surname>
          </string-name>
          ,
          <string-name>
            <given-names>A.</given-names>
            <surname>Hussain</surname>
          </string-name>
          ,
          <string-name>
            <given-names>S.A.</given-names>
            <surname>Samad</surname>
          </string-name>
          ,
          <string-name>
            <given-names>P.J.</given-names>
            <surname>Ker</surname>
          </string-name>
          ,
          <string-name>
            <given-names>M.B.</given-names>
            <surname>Mansor</surname>
          </string-name>
          .
          <article-title>Vision-based traffic sign detection and recognition systems: current trends and challenges</article-title>
          .
          <volume>19</volume>
          (
          <issue>9</issue>
          ):
          <year>2093</year>
          ,
          <year>2019</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          4.
          <string-name>
            <given-names>Z.</given-names>
            <surname>Hu</surname>
          </string-name>
          .
          <article-title>Intelligent road sign inventory (IRSI) with image recognition and attribute computation from video log</article-title>
          .
          <source>Computer‐Aided Civil and Infrastructure Engineering</source>
          ,
          <volume>28</volume>
          (
          <issue>2</issue>
          ):
          <volume>130</volume>
          {
          <fpage>145</fpage>
          ,
          <year>2013</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          5.
          <string-name>
            <given-names>G.A.P.</given-names>
            <surname>Coronado</surname>
          </string-name>
          ,
          <string-name>
            <given-names>M.R.</given-names>
            <surname>Muñoz</surname>
          </string-name>
          ,
          <string-name>
            <given-names>J.M.</given-names>
            <surname>Armingol</surname>
          </string-name>
          , A. de la Escalera,
          <string-name>
            <given-names>J.J.</given-names>
            <surname>Muñoz</surname>
          </string-name>
          , W. van Bijsterveld,
          <string-name>
            <given-names>J.A.</given-names>
            <surname>Bolaño</surname>
          </string-name>
          .
          <article-title>Detection and classification of road signs for automatic inventory systems using computer vision</article-title>
          . Integrated Computer-Aided Engineering,
          <volume>19</volume>
          (
          <issue>3</issue>
          ):
          <volume>285</volume>
          {
          <fpage>298</fpage>
          ,
          <year>2012</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          6.
          <string-name>
            <given-names>D.</given-names>
            <surname>Tabernik</surname>
          </string-name>
          ,
          <string-name>
            <given-names>D.</given-names>
            <surname>Skočaj</surname>
          </string-name>
          .
          <article-title>Deep Learning for Large-Scale Traffic-Sign Detection and Recognition</article-title>
          .
          <source>IEEE Transactions on Intelligent Transportation Systems</source>
          ,
          <year>2019</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          7.
          <string-name>
            <given-names>A.</given-names>
            <surname>Avramović</surname>
          </string-name>
          ,
          <string-name>
            <given-names>D.</given-names>
            <surname>Tabernik</surname>
          </string-name>
          ,
          <string-name>
            <given-names>D.</given-names>
            <surname>Skočaj</surname>
          </string-name>
          .
          <article-title>Real-time Large Scale Traffic Sign Detection</article-title>
          .
          <source>2018 14th Symposium on Neural Networks and Applications (NEUREL)</source>
          ,
          <volume>1</volume>
          {
          <fpage>4</fpage>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          8.
          <string-name>
            <given-names>E.A.</given-names>
            <surname>Roxas</surname>
          </string-name>
          ,
          <string-name>
            <given-names>J.N.</given-names>
            <surname>Acilo</surname>
          </string-name>
          ,
          <string-name>
            <given-names>R.R.P.</given-names>
            <surname>Vicerra</surname>
          </string-name>
          ,
          <string-name>
            <given-names>E.P.</given-names>
            <surname>Dadios</surname>
          </string-name>
          ,
          <string-name>
            <given-names>A.A.</given-names>
            <surname>Bandala</surname>
          </string-name>
          .
          <article-title>Vision-based traffic sign compliance evaluation using convolutional neural network</article-title>
          .
          <source>In 2018 IEEE International Conference on Applied System Invention (ICASI)</source>
          ,
          <volume>120</volume>
          {
          <fpage>123</fpage>
          ,
          <string-name>
            <surname>April</surname>
          </string-name>
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          9.
          <string-name>
            <given-names>P.</given-names>
            <surname>Pandey</surname>
          </string-name>
          ,
          <string-name>
            <given-names>R.</given-names>
            <surname>Kulkarni</surname>
          </string-name>
          .
          <source>Traffic Sign Detection Using Template Matching Technique. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)</source>
          ,
          <volume>1</volume>
          {
          <fpage>6</fpage>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          10.
          <string-name>
            <given-names>Y.</given-names>
            <surname>Ouerhani</surname>
          </string-name>
          [et al.].
          <article-title>Road sign identification and geolocation using JTC and VIAPIX module</article-title>
          .
          <source>Pattern Recognition and Tracking XXIX. International Society for Optics and Photonics</source>
          .
          <volume>10649</volume>
          :
          <issue>106490J</issue>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          11.
          <string-name>
            <given-names>Z.</given-names>
            <surname>Wu</surname>
          </string-name>
          ,
          <string-name>
            <given-names>X.</given-names>
            <surname>Zhou</surname>
          </string-name>
          .
          <article-title>Detecting Street Signs in Cities Based on Object Recognition with Machine Leaning and GIS Spatial Analysis</article-title>
          .
          <source>Proceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities</source>
          ,
          <volume>8</volume>
          {
          <fpage>12</fpage>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation>
          12. A.
          <string-name>
            <surname>Campbell</surname>
            ,
            <given-names>A.</given-names>
          </string-name>
          <string-name>
            <surname>Both</surname>
            ,
            <given-names>Q.C.</given-names>
          </string-name>
          <string-name>
            <surname>Sun</surname>
          </string-name>
          .
          <article-title>Detecting and mapping traffic signs from Google Street View images using deep learning and GIS</article-title>
          . Computers,
          <source>Environment and Urban Systems</source>
          ,
          <volume>77</volume>
          :
          <fpage>101350</fpage>
          ,
          <year>2019</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref13">
        <mixed-citation>
          13. A.
          <string-name>
            <surname>Fahmy</surname>
          </string-name>
          [et al.].
          <source>Role of RFID Technology in Smart City Applications</source>
          . 2019 International Conference on Communications,
          <source>Signal Processing, and their Applications (ICCSPA)</source>
          ,
          <volume>1</volume>
          {
          <fpage>6</fpage>
          ,
          <year>2019</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref14">
        <mixed-citation>
          14.
          <string-name>
            <surname>M.N. Alrafaei</surname>
            ,
            <given-names>A.</given-names>
          </string-name>
          <string-name>
            <surname>Bushager</surname>
          </string-name>
          .
          <article-title>Smart city mobility: Investigation of RFID adoption within transportation management</article-title>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref15">
        <mixed-citation>
          15.
          <string-name>
            <given-names>O.J.</given-names>
            <surname>García</surname>
          </string-name>
          [et al.].
          <source>Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments. Sensors</source>
          ,
          <volume>18</volume>
          (
          <issue>7</issue>
          ):
          <fpage>238</fpage>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref16">
        <mixed-citation>
          16.
          <string-name>
            <given-names>N.</given-names>
            <surname>Ostroglazov</surname>
          </string-name>
          ,
          <string-name>
            <given-names>O.</given-names>
            <surname>Golovnin</surname>
          </string-name>
          ,
          <string-name>
            <given-names>T.</given-names>
            <surname>Mikheeva</surname>
          </string-name>
          .
          <article-title>System analysis and processing of transport infrastructure information</article-title>
          .
          <source>Proceedings of the 5th International Young Scientists Conference on Information Technologies, Telecommunications and Control Systems 2018 (ITTCS</source>
          <year>2018</year>
          ),
          <volume>2298</volume>
          ,
          <year>2018</year>
          .
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>