Significant advancements in ship detection technology in synthetic aperture radar (SAR) images have been made in recent years due to the rapid development of deep learning technology. Nevertheless, there are still certain issues with the current deep learning-based detection algorithms when detecting small-scale, arbitrary-direction, and densely packed ship targets in SAR images. We offer a rotating target detection network based on improved YOLOv8 as a solution to these issues. First, a new small target sampling layer is introduced in YOLOv8 to enhance the model’s ability to detect small-target objects by reducing the down-sampling rate. Second, the learnable weights are introduced to adaptively adjust the feature information at different scales and superimpose shallow features on deep features to better extract feature information when the model conducts feature fusion. Finally, the offset of the predicted bounding box concerning the true bounding box can be expressed more accurately using distribution focal loss and probabilistic intersection-over-union as the loss functions. Extensive experiments are conducted on rotated ship detection dataset in SAR images (RSDD-SAR) and SAR ship detection dataset (SSDD) to demonstrate the effectiveness of the improvement. BP-YOLOv8 achieved an F1 score of 93.01% and 97.48% on the RSDD-SAR and SSDD datasets, with an average precision of 95.87% and 98.82%, respectively.