改进YOLOv5的无人机小目标检测方法研究OA北大核心CSTPCD

Small objects in images captured by UAV have the characteristics of unclear feature information,complex backgrounds,and partial target occlusion,which leads to problems of false detection and missed detection during detec-tion.In response to these problems,an improved UAV small target detection method SDT-YOLOv5 is proposed.Firstly,the dataset images are sliced to increase the proportion of small objects in each slice and improve the recognition ability of small objects.Secondly,a dynamic decoupled detection head is used to introduce dynamic convolution and adaptive receptive field mechanism while decoupling the classification and regression branches of the detection head to achieve stronger feature expression and extraction capabilities.Finally,an optimal transmission allocation method based on intersection-union ratio loss of minimum point distance is proposed.The distance between the upper left corner and lower right corner of the predicted bounding box and the real bounding box is minimized,and then based on the position infor-mation and distance measurement of the bounding box,with the goal of minimizing the total cost,the optimal real bounding box and predicted bounding box matching scheme is found to improve detection accuracy.Through experimental results on the VisDrone2019 dataset,the mAP50 value of the improved YOLOv5 reaches 58.5%,which is 23.2 percentage points higher than the mAP50 value of the original YOLOv5.This shows that the improved method effectively improves the detection accuracy of UAV small objects and can detect small objects more accurately.