A small target detection model for UAV aerial photography based on YOLOv5
In order to solve the problems of high missed detection rate and low detection success rate in UAV small target detection,a small target detection algorithm based on YOLOv5 was proposed.Firstly,the swin transformer module was integrated into the backbone structure and the neck structure respectively,which improved the accuracy of target detection on the basis of reducing the computation-al cost,and could adapt to the detection of small target in UAV aerial photography.Secondly,the con-volutional attention module(CBAM)was introduced to enhance the network's attention for small tar-get features.Finally,the original loss function CIoU was replaced by the SIOU loss function,and the weights of high-quality samples were emphasized to accelerate convergence and improve the regression accuracy.Experimental results show that the detection accuracy on Visdrone2019 dataset is 35.3%after model optimization,which is 5.2%higher than that of YOLOv5.Compared with other classical and ad-vanced algorithms,SWCBSI-YOLO algorithm performs well and meets the detection requirements of small targets for UAV aerial photography.
UAV aerial photographysmall target detectionYOLOv5transformerattention mecha-nismloss function
国家科技期刊平台
NSTL
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