Abstract: In a real remote sensing image, the target is distributed in any direction and it is difficult for the original YOLOv5 network to accurately express the location and range of the target and the detection speed is moderate. To solve these problems, a remote sensing image rotating target detection model, YOLOv5-Left-Rotation, was proposed. First, the transformer self-attention mechanism was used to make the model pay more attention to the targets of interest. In addition, Mosaic data were enhanced in the image preprocessing, and the improved Non-Maximum Suppression algorithm was used in post-processing. Second, an angle loss function was introduced to increase the output dimensions of the network, and the prediction box of the rotating rectangle was obtained. Finally, in the shallow stage of the network model, a sliding window branch was added to improve the detection efficiency of large-sized remote sensing sparse targets. The experimental datasets were the self-made aircraft dataset CASIA-plane78 and the public ship dataset HRSC2016. The results show that the average accuracy of the improved rotating target detection algorithm is improved by 3.175% compared with that of the original model, and the reasoning speed is improved by 13.6% in a large multispectral image swept by a Jilin-1 satellite. It can optimally reduce the redundant background information and more accurately detect the densely arranged and irregularly distributed areas of objects of interest in optical remote sensing images.