LSD-Det: A Lightweight Detector for Small Ship Targets in SAR Images

Synthetic Aperture Radar (SAR) plays a vital role in ship safety monitoring and marine environmental protection. However, ship targets in SAR images are often small, have blurred edges, and suffer from strong background interference, making it difficult for traditional detection algorithms to balance accuracy and real-time performance. To address these challenges, this paper proposes LSD-Det, a lightweight SAR ship detection model improved from YOLOv8n. First, the YOLOv8n architecture is streamlined to reduce redundancy, enhancing suitability for SAR detection. Then, a Grouped Split Enhanced Channel Attention (GSECA) module is introduced in the backbone, combining average and max pooling with channel shuffle to improve recognition of small targets and suppress background noise. Additionally, a Global-Enhanced Dilated Wavelet Transform (GEDWT) module is embedded in the neck’s C2f structure to enhance multi-scale feature representation with minimal computational overhead. Furthermore, the original CIoU loss is replaced with PIoUv2, which accelerates convergence and improves bounding box regression accuracy. Experiments conducted on two publicly available SAR ship detection datasets, SSDD and HRSID, demonstrate that LSD-Det significantly reduces computational cost while maintaining high detection performance. Specifically, parameters are reduced by 65.7%, GFLOPs by 20.7%, and inference speed is notably improved. LSD-Det also improves mAP on SSDD by 1.2% ([email protected]) and 2.2% ([email protected]:0.95), with corresponding gains of 0.8% and 1.3% on HRSID. The experimental outcomes demonstrate that the proposed approach maintains a desirable trade-off between precision and computational efficiency, making it suitable for real-time SAR ship detection under limited-resource conditions.