Research on remote sensing target detection algorithm based on improved YOLOv5

Accurate and fast detection of targets in remote sensing images has always been a challenge due to the small size and diverse scales of the targets in the images. In order to improve the detection accuracy and shorten the detection time, this paper proposes a remote sensing image target detection method based on the CA attention mechanism and the lightweight network Slim-neck, which improves the target detection ability of YOLOv5s network. This paper focuses on enhancing the neck portion of the YOLOv5s. First, to improve the network's capacity for feature learning, multiple CA attention mechanisms are added to Neck. Secondly, the GSConv and VoVGSCSP modules are introduced to replace the last Conv and C3 modules in the neck. These improvements have allowed the network to increase detection speed along with detection precision. Experiments on the dataset DOTA-v1.0 show that the improved YOLOv5s model increases the detection precision by 3.5% and reduces the computational complexity by 2.5%.