Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.243094

Keywords:

object recognition, deep convolutional neural network, aerial photograph, unmanned aerial vehicle

Abstract

Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained.

The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs.

In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems

Author Biographies

Vadym Slyusar, Central Scientific Research Institute of the Army of the Armed Forces of Ukraine

Doctor of Technical Sciences, Professor

Research Institute Group

Mykhailo Protsenko, Central Scientific Research Institute of the Army 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

Pavlo Kovalov, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Fire and Rescue Training

Pavlo Borodych, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Fire and Rescue Training

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

Serhii Vazhynskyi, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor

Scientific Center Air Force

Oleg Bogatov, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Metrology and Life Safety

Kirill Khrustalev, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Computer-Integrated Technologies, Automation and Mechatronics

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Published

2021-10-31

How to Cite

Slyusar, V., Protsenko, M., Chernukha, A., Kovalov, P., Borodych, P., Shevchenko, S., Chernikov, O., Vazhynskyi, S., Bogatov, O., & Khrustalev, K. (2021). Improvement of the model of object recognition in aero photographs using deep convolutional neural networks. Eastern-European Journal of Enterprise Technologies, 5(2 (113), 6–21. https://doi.org/10.15587/1729-4061.2021.243094

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