DS-YOLO: A SAR Ship Detection Model for Dense Small Targets

Abstract

Detecting dense small targets in Synthetic Aperture Radar (SAR) images has always been a challenge in ship target detection. To address this issue, this paper proposes a ship target detection model for SAR images, named DS-YOLO, which is based on the YOLO11 network architecture. The model introduces Space-to-Depth Convolution (SPDConv) module to enhance the detection capability of small targets. Additionally, a new module, Cross Stage Partial-Partial Pyramid Attention (CSP-PPA), is incorporated to improve the model's ability to extract features at multiple scales and suppress confusing backgrounds. The loss function is optimized using a bounding box loss based on Adaptive Weighted Normalized Wasserstein distance (AWNWD), enhancing the model's adaptability to images of varying quality. Finally, experiments were conducted on the standard datasets HRSID and SAR-Ships dataset to validate the robustness and reliability of the DS-YOLO model. The experimental results show that, compared to YOLO11n, DS-YOLO achieved an mAP0.5:0.95 of 68.6% on the SAR-Ships dataset and 69.9% on the HRSID, representing improvements of 1.6% and 0.8%, respectively. Additionally, on these small-target datasets, DS-YOLO achieved an mAP0.5:0.95 of 50.8% and 60.4%, representing improvements of 4.2% and 1.2%, respectively, demonstrating higher detection accuracy.