Improved Military Object Detection Algorithm in Complex Scenes Based on YOLOV5s

SUN Tian; ZHANG Yi; HAN Xudong; XIA Zhiyu; WANG Guoping

doi:10.15892/j.cnki.djzdxb.2025.01.006

Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 1: 45 - 52

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.01.006

Improved Military Object Detection Algorithm in Complex Scenes Based on YOLOV5s

SUN Tian ^,¹ ,
ZHANG Yi ^,¹ ,
HAN Xudong ¹ ,
XIA Zhiyu ² ,
WANG Guoping ¹

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¹ Xi'an Mordern Control Technology Research Institute,Xi'an 710065,Shaanxi,China
² School of Automation,Northwestern Polytechnical University,Xi'an 710129,Shaanxi,China

Received date: 2024-04-16

Online published: 2025-03-12

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Abstract

Accurately and quickly detecting military targets in complex scenarios has important military value in perceiving battlefield situations, conducting reconnaissance and early warning analysis, and providing precise missile guidance. A multi-scale object detection algorithm AEM-YOLOV5 (AFPN-EMA-MPDIoU-YOLOV5) based on improvements to YOLOV5s is proposed to address the issues of poor feature learning ability, low detection accuracy, and high computational complexity in existing algorithms. Firstly, the AFPN asymptotic feature pyramid network is introduced into the neck network to gradually fuse the detailed information at the bottom of the image and the high-level semantic features at the top, enhancing the network feature fusion effect. Secondly, an EMA attention mechanism module is added before each detection branch to aggregate pixel level features across spaces, improving the level of attention to multi-scale targets in complex scenes. Finally, MPDIoU is used to replace the original C_IoU bounding box loss function in YOLOV5, solving the problem of C_IoU degradation when the predicted box aspect ratio is the same but the absolute value is different, making the regression results more accurate. The experimental results show that the improved algorithm performs well on the RSOD dataset, P_mAP50 reaches 94.5%, FPS reaches 42 frame/s, and model size is 14.8 MB. Compared with existing algorithms, the improved algorithm significantly improves its performance, meets the real-time requirements of military target detection, and the model is lightweight.

Key words： military object detection; YOLOV5; asymptotic feature pyramid network; multi scale targets; attention mechanism

Cite this article

SUN Tian , ZHANG Yi , HAN Xudong , XIA Zhiyu , WANG Guoping . Improved Military Object Detection Algorithm in Complex Scenes Based on YOLOV5s[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(1) : 45 -52 . DOI: 10.15892/j.cnki.djzdxb.2025.01.006

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