Journal article Open Access

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

Vadym Slyusar; Mykhailo Protsenko; Anton Chernukha; Stella Gornostal; Sergey Rudakov; Serhii Shevchenko; Oleksandr Chernikov; Nadiia Kolpachenko; Volodymyr Timofeyev; Roman Artiukh

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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • El-Baz, A., Jiang, X., Jasjit, S. (Eds.) (2016). Biomedical image segmentation. CRC Press. doi: https://doi.org/10.4324/9781315372273

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