In this paper, we propose a rotating target detection algorithm using the improved YOLOv5m to solve the problems of low accuracy and poor target direction in the rotating target detection of optical remote sensing images. First, we integrate the attention mechanism module into the network to improve the ability of the model to extract important features. Second, we consider the contribution of the feature fusion at each node in the feature fusion module, with the addition of a skip connection with the same feature scale. Finally, the angles are discretized through densely coded labels due to the angles and boundaries in rotation detection. The experimental results show that this algorithm achieves a detection accuracy of 82.75% for a subset of DOTA data, indicating an improvement of 11.73 percentage points compared with the original YOLOv5m network when the model computation is reduced slightly. Furthermore, we achieved a detection accuracy of 88.89% in the HRSC2016 ship dataset. That is, the algorithm can effectively improve the accuracy of the rotation detection of optical remote sensing images.