Abstract: With the development of science and technology,drones are more and more widely used,and the realization of accurate motion capture of drones has become its core technology.When the optical motion capture system detects and tracks the UAV,due to the interference of complex environment,flight speed and other aspects,the Marker point pasted by the UAV will be inaccurate.In order to solve this problem,an improved object detection algorithm EAP-YOLOv8 based on YOLOv8 is proposed to improve the accuracy of Marker point recognition detection.Firstly,a new channel attention mechanism MAP-ECA is constructed in the backbone part,which enhances the global perspective information and the characteristics of different scales,and improves the detection ability of small targets.Secondly,on the basis of the original detection head,the multi-level adaptive feature fusion is used to form a new detection head,D-SASFF,and the multi-scale fusion is used to strengthen the feature information of small targets.Finally,the loss function PIoUv3 is designed,which accelerates the convergence speed of the model and improves the detection ability of small targets.In order to verify the effectiveness of the EAP-YOLOv8 algorithm,experiments are carried out on the self-made dataset,and the results show that the EAP-YOLOv8 algorithm reaches 96.5% and 50.2% on mAP@0.5 and mAP@0.5:0.95,respectively,which is significantly improved compared with other algorithms.On this basis,the tracking accuracy of Marker points is significantly improved by combining the multi-target tracking algorithm ByteTrack,and the tracking experiments are carried out on the public dataset MOT16,and the results show that the new model reaches 37.60%,25.64% and 80.76% on HOTA,MOTA and MOTP,respectively,which is significantly improved compared with the current algorithms,providing an effective way for the subsequent accurate tracking of UAVs.

Key words: EAP-YOLOv8, Drone detection, Marker point, Small target detection, Multi-target tracking, ByteTrack