基于改进YOLOv5的军事目标识别方法OACSTPCD

To address the problem of false detection and missed detection due to background interference and small scale of military targets in battlefield environment,a military target recognition method CB-YOLOv5 based on improved YOLOv5 is pro-posed.The feature extraction backbone network is reconstructed by using coordinate attention mechanism to enhance the feature ex-traction capability of the network for military targets in complex background.BiFPN is introduced in the feature fusion network to re-duce the loss of shallow feature information and improve the weak targets can be detected.Experiments under the self-built dataset show that the improved algorithm mAP reaches 93.8%,which is 3.5%better than the original model,and can effectively identify multi-scale military targets in the battlefield environment.