An Object Detection Method for Military Target Based on Improved YOLOv4

GUO Haochang; YU Li; LIU Zhentao

doi:10.15892/j.cnki.djzdxb.2021.06.011

Journal of Projectiles, Rockets, Missiles and Guidance >

2021 , Vol. 41 >Issue 6: 53 - 58

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2021.06.011

An Object Detection Method for Military Target Based on Improved YOLOv4

GUO Haochang ,
YU Li ,
LIU Zhentao

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Xi'an Modern Control Technology Research Institute,Xi'an 710065, China

Received date: 2020-09-15

Online published: 2025-02-07

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Abstract

In order to solve the problem of traditional object detection such as complex background of object; various object scale;object occlusion and motion blur, we use deep learning algorithm for object detection. According to the data set established by the author, the YOLOv4 algorithm is refined in some aspects such as improving the network architecture, changing the number of anchor boxes and using spatial attention module. As the results, the improved YOLOv4 achieves the highest value of mAP comparing with other methods and the frame rate is 38.2 fps which satisfies the requirements of military object detection.

Key words： military object detection; deep learning algorithm; YOLOv4; military target data set

Cite this article

GUO Haochang , YU Li , LIU Zhentao . An Object Detection Method for Military Target Based on Improved YOLOv4[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2021 , 41(6) : 53 -58 . DOI: 10.15892/j.cnki.djzdxb.2021.06.011

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