Journal of Radio Electronics. eISSN 1684-1719. 2025. №2
Full text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2025.2.7
MILITARY OBJECTS DETECTION USING A CONVOLUTIONAL
NEURAL NETWORK ON RADAR IMAGES FORMED
IN JAMMING CONDITIONS
I.F. Kupryashkin
Military Educational and Scientific Center of the Air Force
«N.E. Zhukovsky and Y.A. Gagarin Air Force Academy»,394064, Russia, Voronezh, Starykh Bolshevikov St., 64
The paper was received October 24. 2024.
Abstract. The MSTAR objects detection on noised radar images using the YOLOv4-tiny detector results are presented. It has been shown that on a homogeneous background detector work is satisfactory when the signal-to-noise ratio is 10 dB or more, and completely disrupted when the signal-to-noise ratio is less than 0 dB.
Key words: deep convolutional neural network, transfer learning, radar image, classification accuracy, object detection.
Corresponding author: Kupryashkin Ivan Fedorovich, ifk78@mail.ru
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For citation:
Kupryashkin I.F. Military objects detection using a convolutional neural network on radar images formed in jamming conditions. // Journal of Radio Electronics. – 2025. – №. 2. https://doi.org/10.30898/1684-1719.2025.2.7 (In Russian)