Soldier identification based on improved YOLOv5 algorithm in battlefield environment

Yawen Jiang; Jie Zhou; Bingqin Liu; Xiaomin Shi; Yuxiao Yan; Hongyan Wang; Youchen Fan

doi:10.1117/12.2675309

25 May 2023 Soldier identification based on improved YOLOv5 algorithm in battlefield environment

Yawen Jiang, Jie Zhou, Bingqin Liu, Xiaomin Shi, Yuxiao Yan, Hongyan Wang, Youchen Fan

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126361R (2023) https://doi.org/10.1117/12.2675309
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

For soldier recognition in the battlefield environment, there are factors such as camouflage and object occlusion, thus leading to incomplete feature information and poor recognition effect. In this paper, we first construct a soldier target dataset conforming to the characteristics of the battlefield environment by analyzing the factors influencing the battlefield environment. Then this paper improves the yolov5 algorithm to detect soldier recognition quickly by adding a channel attention mechanism and improving the spatial pyramid pooling structure. The implementation results show that the predicted mAP value can reach 0.946 with a 3% improvement, the recall rate reaches 0.86, and the detection speed is improved by 5%. It achieves better recognition of soldiers in the battlefield environment.

Citation Download Citation

Yawen Jiang, Jie Zhou, Bingqin Liu, Xiaomin Shi, Yuxiao Yan, Hongyan Wang, and Youchen Fan "Soldier identification based on improved YOLOv5 algorithm in battlefield environment", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126361R (25 May 2023); https://doi.org/10.1117/12.2675309

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