Target Detection Based on Improved YOLOv3 UAV Image
Objects in UAV aerial images are usually very small,with blurry boundaries,complex backgrounds and changing lighting conditions,so the detection accuracy of YOLOv3 algorithm is relatively low.Therefore,constructing a four level BiFPN can not only make equal contributions to the fused output features,but also make the Neck part become an FPN+PAnet structure;and meanwhile,it makes full use of the fusion information of low-level and high-level features to improve the performance of feature extraction and target detection.By adding a smaller detection layer,the model can achieve four priori boxes,and further,there is a higher probability that a priori box with good matching degree for the target object will appear,which will make the model more easier to learn.The experimental results show that the proposed UAV image target detection model(YOLOv3_Drone)in VisDrone-2019's mAP is 3.83％higher than that of YOLOv3 algorithm,which proves the effectiveness of this method.
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