基于改进YOLOv7的无人机图像目标检测算法OA

To solve the problems of small targets,mutual occlusion,and less feature information in UAV images,which lead to low detection accuracy,an improved YOLOv7 UAV image target detection algorithm is proposed.CoordConv is added to the neck and detection head,which can better sense the position information of the target in the feature map;the P2 detection layer is added to reduce the loss of small target features and improve the detection ability of small targets;multiple information flow fusion attention-Spatial and Channel Attention Mechanism(SC A)is proposed to dynamically adjusts the focus on spatial information flow and semantic information flow to obtain more comprehensive feature information to improve the ability to capture targets;the loss function is replaced with SIoU to speed up model convergence.A comparison experiment is conducted on the public dataset VisDrone2019.The mAP50 value of the proposed algorithm is 4％higher than that of YOLOv7,reaching 52.4％,and the FPS is 37.The ablation experiments verify that each module improves the detection accuracy.Experiments show that the improved algorithm can better detect objects in UAV images.