An improved ship target detection algorithm DD-YOLO based on YOLOv8 was proposed in order to address the challenges of small target size, significant scale variations and complex background noise in synthetic aperture radar (SAR) images for ship detection. An enhanced C2f module was incorporated in the backbone network to strengthen multi-scale feature extraction and fusion capabilities, combined with a newly designed SPA module to optimize gradient flow information propagation, significantly improving multi-scale target detection performance. A more lightweight dynamic upsampling approach was adopted in the neck network, which reduced computational overhead and model complexity while enhancing the recognition of small ships in complex backgrounds. A multi-dimensional attention mechanism was integrated in the detection head and lightweight processing was conducted to improve the model’s sensitivity to key features in complex backgrounds, thereby increasing detection accuracy. Experiments conducted on two public datasets, HRSID and SSDD, demonstrate that DD-YOLO achieves mAP₅₀ scores of 92.2% and 98.5%, respectively, representing improvement of 2% and 2.2% over the baseline model. The model complexity is significantly lower than that of mainstream algorithms, achieving an optimal balance between accuracy and efficiency.