Small object detection algorithm for unmanned aerial vehicle image based on improved YOLOv8
Aiming at the problem of low detection accuracy caused by small object size and little feature information in unmanned aerial vehicle images,an improved small object detection algorithm of YOLOv8n is proposed.Firstly,the Wise-IoU loss function is introduced,which enhances the network's focus on ordinary quality anchor frames through a dynamic non-monotonic focusing mechanism,improving the generalization ability of the algorithm.Sec-ondly,in order to improve the accuracy of small object detection,a new feature fusion structure,the Bi-SODL structure,is constructed by adding a small object detection layer(SODL)and bidirectional feature pyramid net-work(BiFPN).SODL enables the network to capture the shallow feature information of the small object more ade-quately.BiFPN can achieve the information exchange and fusion between feature layers of different scales.Finally,LSKBlock attention mechanism is introduced,which processes the input features through spatial selection mecha-nism and weighting,further improving the performance and robustness of small object detection.The experimental results show that the detection accuracy metrics P,mAP_0.50 and mAP_0.50∶0.95 on the VisDrone2019 data-set are increased by 6.4％,8.3％and 5.2％respectively,and the number of parameters is reduced by 25.78％.The improved measures make the detection performance better than many mainstream algorithms,which proves the effectiveness of the improved algorithm.
small object detectionYOLOv8nfeature fusionattention mechanism
国家科技期刊平台
NSTL
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