Optimization research of UAV target detection algorithm based on deep learning

With the rapid development of deep learning, UAV target detection technology based on computer vision and artificial intelligence has been widely used in practice. However, due to the instability of UAV movement, limited by load and endurance, the development of UAV target detection is slow, and there are challenges such as significant changes in target scale, occlusion between objects, and changes in target density. This paper builds on the network model structure of YOLOv5 to address these challenges. It adds a detection head generated from low-level feature layers and high-resolution combined feature maps to detect tiny objects. We utilize the Bifpn network structure and a weighted fusion splicing approach to fuse more features and introduce an improved Coordinate Attention to obtain location information for feature enhancement accurately. Extensive experiments on the Visdrone2021 dataset show that the model achieves good results in UAV target detection and is helpful for tiny and occluded target detection.