The detection speed and accuracy of small targets captured by UAV aerial photography cannot be considered at the same time.To address this problem,a new algorithm based on the Yolov5 algorithm,namely Yolov5_GBCS,is proposed for small-target detection of UAV-captured images.In the new algorithm,an additional detector head is added to enhance the feature fusion effect of small targets.In the backbone network,the GhostConv convolution module and GhostBottleneckC3 module are used to replace some original Conv modules and C3 modules for extracting rich and re-dundant features,respectively,to improve the model efficiency.The weighted bidirectional feature pyramid network structure is introduced to enhance the detection accuracy of small targets.The lightweight convolutional block attention module is introduced into the backbone and neck networks to focus on important features and suppress unnecessary fea-tures to boost the ability of small-target feature expression.The Soft-NMS algorithm is used to replace the NMS for re-ducing the miss detection rate of small targets in dense scenes.Experimental results on the VisDrone 2019 dataset show that the Yolov5_GBCS algorithm integrating all improved methods enhances the detection accuracy and effectively im-proves the detection speed.The mAP of the subject model has been increased from 38.5%to 43.2%,and the detection speed from 53 f/s to 59 f/s.Therefore,the Yolov5_GBCS algorithm can effectively recognize small targets in the image captured by UAV aerial photography.
维普
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