A novel detection algorithm (efficient multi-scale attention (EMA) and small object detection based on YOLOv5, ES-YOLOv5) was proposed by targeting small ship targets in SAR scenes aiming at the issues of inconspicuous imaging features and low detection accuracy caused by arbitrary orientation of small targets in synthetic aperture radar (SAR) imaging. A small target detection layer was added to adjust the receptive field size, making it more suitable for capturing small target scale features and facilitating multi-scale fusion. An EMA mechanism was introduced to focus on key target information and enhance feature representation capability. The circular smooth label (CSL) technique was utilized to adapt to the periodicity of angles, achieving high-precision angle classification. The experimental results demonstrate that the proposed method achieves an average detection accuracy of 90.9% at an intersection over union (IoU) threshold of 0.5 on the RSDD-SAR dataset. The algorithm outperforms the baseline algorithm YOLOv5 by 6% in improving the precision of detecting small SAR ship targets, significantly enhancing the model’s detection performance.