Object Detection Algorithm for UAV Aerial Image Based on Improved YOLOv8
To solve the problem that the existing UAV aerial image target detection algorithm has low detection accuracy and complex model,an improved YOLOv8 target detection algorithm is proposed.Multi-scale attention EMA is introduced into the backbone network to capture detailed information to improve the feature extraction ability and C2f module is improved to reduce the calculation amount of the model.The lightweight Bi-YOLOv8 feature pyramid network structure is proposed to improve the neck of YOLOv8,the multi-scale feature fusion ability of the model is enhanced,and the detection accuracy of the network for small targets is improved.WIoU Loss is used to optimize the original network loss function,and a dynamic non-monotonic focusing mechanism is introduced to improve the generalization ability of the model.Experiments on UAV aerial image data set VisDrone2019 show that the mAP50 of the proposed algorithm is 40.7％,which is 1.5％higher than YOLOv8s,and the number of parameters is reduced by 42％.The accuracy and speed are improved compared with other advanced target detection algorithms,which proves the effectiveness and advanced nature of the proposed algorithm.
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