UAV IMAGE TARGET DETECTION BASED ON ASPP-YOLOV4 MULTI-SCALE FUSION

Aimed at the problems of complex background, large number of small targets and high missed and false detection rate of UAV video image, a small target detection algorithm based on improved YOLOv4 is proposed. An improved attention mechanism was added to enhance the ability to focus on small target information. A detection head was added and fused with the feature map of the backbone network to obtain the semantic information of small targets. The improved ASPP network was used to replace the ordinary convolution block for down sampling to increase the receptive field and reduce the loss of information. The experimental results on the VisDrone2019 dataset show that the map of ASPP-YOLOv4 is 3.82 percentage points higher than that of YOLOv4, which significantly improves the detection accuracy of small targets.