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

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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

Section

Information and controlling system