YOLOv4_Drone: UAV image target detection based on an improved YOLOv4 algorithm

Advanced communications and networks have greatly improved the user experience, and unmanned aerial vehicle (UAV) are an important technology that supports people's daily life and military activities. Since target detection in UAV images is complicated by a complex background, small targets, and target occlusion, the detection accuracy of the You Only Look Once(YOLO) v4 algorithm is relatively low. Therefore, hollow convolution is used to resample the feature image to improve the feature extraction and target detection performance. In addition, the ultra-lightweight subspace attention mechanism (ULSAM) is used to derive different attention feature maps for each subspace of the feature map for multi-scale feature representation. Finally, soft non-maximum suppression (Soft-NMS) is introduced to minimize the occurrence of missed targets due to occlusion. The experimental results prove that the proposed UAV image target detection model (YOLOv4_Drone) has 5% improved to the YOLOv4 algorithm, demonstrating the effectiveness of the method.

Automated summary

A new UAV image target detection algorithm is proposed, which improves the detection accuracy through techniques such as hollow convolution, ultra-lightweight subspace attention mechanism, and soft non-maximum suppression. Experimental results show a 5% improvement compared to the YOLOv4 algorithm.