Soldier identification based on improved YOLOv5 algorithm in battlefield environment

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.