SAR Ship Target Instance Segmentation Based on SISS-YOLO

Abstract

Maritime transportation, fishing, scientific research, and other activities rely on various types of ships and platforms, making precise monitoring of ships at sea essential. Synthetic Aperture Radar (SAR) is minimally affected by weather conditions and darkness and is used for ship detection in maritime environments. This study analyzes the differences in backscatter characteristics among various ship types in SAR images and proposes SISS-YOLO, an enhanced model based on YOLOv8. The proposed method addresses the challenge of ship instance segmentation in SAR images involving multiple polarizations, scenarios, and classes. First, the backbone structure was optimized by incorporating additional pooling layers and refining the activation functions. Second, the Coordinate Attention (CA) module was integrated into the C2F template, embedding spatial position information into the channel attention mechanism. Third, a slide loss function was adopted to address the class imbalance across ship categories. The experiments were conducted on the OpenSARShip2.0 dataset, which includes cargo, tanker, passenger and engineering ships. The results show that the SISS-YOLO achieves a mask precision of 88.3%, a mask recall of 86.4% and a mask mAP50 of 93.4% for engineering ships. Compared with YOLOv8m, SISS-YOLO achieved improvements of 15.7% in mask precision and 8.8% in mask recall. The model trained on the OpenSARShip2.0 dataset was directly applied to the FUSAR-Ship1.0 dataset, demonstrating a degree of robustness. When applied to SAR data, the SISS-YOLO model achieves high detection accuracy, demonstrating generalization.