Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.238601

Keywords:

neural network, discrete wavelet transform, monitored objects, unmanned aircraft system

Abstract

The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets.

The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

Author Biographies

Vadym Slyusar, Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine

Doctor of Technical Sciences, Professor

Research Institute Group

Mykhailo Protsenko, Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine

PhD, Senior Researcher

Office of Special Forces

Anton Chernukha, National University of Civil Defence of Ukraine

PhD

Department of Fire and Rescue Training

Stella Gornostal, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Fire Prevention in Settlements

Sergey Rudakov, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Fire Prevention in Settlements

Serhii Shevchenko, National University of Civil Defence of Ukraine

PhD

Department of Fire Tactics and Rescue Operations

Oleksandr Chernikov, Kharkiv National Automobile and Highway University

Doctor of Technical Sciences, Professor

Department of Engineering and Computer Graphics

Nadiia Kolpachenko, Kharkiv Petro Vasylenko National Technical University of Agriculture

PhD, Associate Professor

Department of Technological Systems of Repair Production Named after O. Sidashenko

Volodymyr Timofeyev, O.M. Beketov National University of Urban Economy in Kharkiv

Doctor of Technical Sciences, Professor

Department of Automatics and Computer-Integrated Technologies

Roman Artiukh, State Enterprise “Southern State Design and Research Institute of Aviation Industry”

PhD, Director

References

  1. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Maksymenko, N., Meleshchenko, R. et. al. (2020). Mathematical model of determining a risk to the human health along with the detection of hazardous states of urban atmosphere pollution based on measuring the current concentrations of pollutants. Eastern-European Journal of Enterprise Technologies, 4 (10 (106)), 37–44. doi: https://doi.org/10.15587/1729-4061.2020.210059
  2. Vambol, S., Vambol, V., Kondratenko, O., Suchikova, Y., Hurenko, O. (2017). Assessment of improvement of ecological safety of power plants by arranging the system of pollutant neutralization. Eastern-European Journal of Enterprise Technologies, 3 (10 (87)), 63–73. doi: https://doi.org/10.15587/1729-4061.2017.102314
  3. Vambol, S., Vambol, V., Kondratenko, O., Koloskov, V., Suchikova, Y. (2018). Substantiation of expedience of application of high-temperature utilization of used tires for liquefied methane production. Journal of Achievements in Materials and Manufacturing Engineering, 2 (87), 77–84. doi: https://doi.org/10.5604/01.3001.0012.2830
  4. Semko, A., Rusanova, O., Kazak, O., Beskrovnaya, M., Vinogradov, S., Gricina, I. (2015). The use of pulsed high-speed liquid jet for putting out gas blow-out. The International Journal of Multiphysics, 9 (1), 9–20. doi: https://doi.org/10.1260/1750-9548.9.1.9
  5. Pospelov, B., Andronov, V., Rybka, E., Skliarov, S. (2017). Design of fire detectors capable of self-adjusting by ignition. Eastern-European Journal of Enterprise Technologies, 4 (9 (88)), 53–59. doi: https://doi.org/10.15587/1729-4061.2017.108448
  6. Kustov, M. V., Kalugin, V. D., Tutunik, V. V., Tarakhno, E. V. (2019). Physicochemical principles of the technology of modified pyrotechnic compositions to reduce the chemical pollution of the atmosphere. Voprosy Khimii i Khimicheskoi Tekhnologii, 1, 92–99. doi: https://doi.org/10.32434/0321-4095-2019-122-1-92-99
  7. Popov, O., Іatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V. et. al. (2019). Analysis of Possible Causes of NPP Emergencies to Minimize Risk of Their Occurrence. Nuclear and Radiation Safety, 1 (81), 75–80. doi: https://doi.org/10.32918/nrs.2019.1(81).13
  8. Yang, X., Lin, D., Zhang, F., Song, T., Jiang, T. (2019). High Accuracy Active Stand-off Target Geolocation Using UAV Platform. 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). doi: https://doi.org/10.1109/icsidp47821.2019.9172919
  9. Thepade, S. D., Dewan, J. H., Erandole, S. S., Jadhav, S. R. (2015). Extended performance comparison of self mutated hybrid wavelet transforms in image compression with hybrid wavelet transforms & orthogonal transforms. 2015 Global Conference on Communication Technologies (GCCT). doi: https://doi.org/10.1109/gcct.2015.7342675
  10. Zhu, J., Wang, J., Zhu, Q., Liu, P., Li, S. (2018). Reconstruction of Compressed Sensed Images with Multiple-Image Pattern Low-Rank Tensor. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). doi: https://doi.org/10.1109/icivc.2018.8492809
  11. Van, F., Patrick, J. (2019). An introduction to digital images. Discrete Wavelet Transformations: An Elementary Approach with Applications. Wiley, 69–123. doi: https://doi.org/10.1002/9781119555414.ch3
  12. Van, F., Patrick, J. (2019). Biorthogonal wavelet transformations. Discrete Wavelet Transformations: An Elementary Approach with Applications. Wiley, 261–320. doi: https://doi.org/10.1002/9781119555414.ch7
  13. Krishnaswamy, R., NirmalaDevi, S. (2020). Efficient medical image compression based on integer wavelet transform. 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII). doi: https://doi.org/10.1109/icbsii49132.2020.9167597
  14. Thepade, S. D., Erandole, S. (2013). Effect of tiling in image compression using wavelet transform & hybrid wavelet transform for cosine & kekre transforms. 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN). doi: https://doi.org/10.1109/ice-ccn.2013.6528604
  15. Thepade, S. D., Dewan, J. H., Lohar, A. T. (2013). Extended performance comparison of hybrid wavelet transform for image compression with varying proportions of constituent transforms. 2013 15th International Conference on Advanced Computing Technologies (ICACT). doi: https://doi.org/10.1109/icact.2013.6710497
  16. Paul, A., Khan, T. Z., Podder, P., Ahmed, R., Rahman, M. M., Khan, M. H. (2015). Iris image compression using wavelets transform coding. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). doi: https://doi.org/10.1109/spin.2015.7095407
  17. Nashat, A. A., Hussain Hassan, N. M. (2016). Image compression based upon Wavelet Transform and a statistical threshold. 2016 International Conference on Optoelectronics and Image Processing (ICOIP). doi: https://doi.org/10.1109/optip.2016.7528492
  18. Ahanonu, E., Marcellin, M., Bilgin, A. (2020). Lossless Multi-component Image Compression Based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. 2020 Data Compression Conference (DCC). doi: https://doi.org/10.1109/dcc47342.2020.00043
  19. Il'yasov, B. G., Makarova, E. A., Zakieva, E. Sh., Gabdullina, E. R. (2021). Metody iskusstvennogo intellekta v programmnyh prilozheniyah: laboratorniy praktikum po distsiplinam «Metody iskusstvennogo intellekta v upravlenii», «Intellektual'noe upravlenie slozhnymi obektami», «Intellektual'noe upravlenie slozhnymi tekhnicheskimi obektami», «Metody iskusstvennogo intellekta v upravlenii tekhnicheskimi obektami», «Programmnye sistemy i kompleksy v upravlenii kachestvom». Ufa: UGATU. Available at: https://www.ugatu.su/media/uploads/MainSite/Ob%20universitete/Izdateli/El_izd/2021‐52.pdf
  20. Protsenko, M. M., Pavlunʹko, M. Y., Moroz, D. P., Brzhevsʹka, Z. M. (2019). Procedure of signal filtering based on wavelet transformation. Modern Information Security, 1 (37), 64–69. doi: https://doi.org/10.31673/2409-7292.2019.016469
  21. Protsenko, M., Kurtseitov, Т., Pavlunko, M., Brzhevska, Z. (2018). Wavelet transforms application for digital signal analysis. Use of packet wavelet transformation for radio signals processing. Modern Information Security, 3 (35), 11–15. doi: https://doi.org/10.31673/2409-7292.2018.031115
  22. El-Baz, A., Jiang, X., Jasjit, S. (Eds.) (2016). Biomedical image segmentation. CRC Press. doi: https://doi.org/10.4324/9781315372273

Downloads

Published

2021-08-31

How to Cite

Slyusar, V., Protsenko, M., Chernukha, A., Gornostal, S., Rudakov, S., Shevchenko, S., Chernikov, O., Kolpachenko, N., Timofeyev, V., & Artiukh, R. (2021). Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform. Eastern-European Journal of Enterprise Technologies, 4(9(112), 65–77. https://doi.org/10.15587/1729-4061.2021.238601

Issue

Vol. 4 No. 9(112) (2021): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2021 Vadym Slyusar, Mykhailo Protsenko, Anton Chernukha, Stella Gornostal, Sergey Rudakov, Serhii Shevchenko, Oleksandr Chernikov, Nadiia Kolpachenko, Volodymyr Timofeyev, Roman Artiukh

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.