YOSDet: A YOLO-Based Oriented Ship Detector in SAR Imagery

초록

Highlights What are the main findings? YOSDet, a YOLO-based oriented ship detector, effectively handles arbitrarily oriented ships in SAR imagery, achieving high detection accuracy across SSDD+, HRSID, and SRSDD-v1.0 benchmarks. The model integrates a dynamic aggregation module (DAM), an objective-guided detection head (OGDH), and a localization quality estimator (LQE), improving prediction consistency under noisy SAR imaging conditions. What are the implications of the main findings? The results demonstrate robust generalization for both inshore and offshore scenarios, making the framework suitable for real-time maritime surveillance. This work provides a practical framework for oriented ship detection in complex SAR environments, highlighting the potential of deep learning in remote sensing applications.Highlights What are the main findings? YOSDet, a YOLO-based oriented ship detector, effectively handles arbitrarily oriented ships in SAR imagery, achieving high detection accuracy across SSDD+, HRSID, and SRSDD-v1.0 benchmarks. The model integrates a dynamic aggregation module (DAM), an objective-guided detection head (OGDH), and a localization quality estimator (LQE), improving prediction consistency under noisy SAR imaging conditions. What are the implications of the main findings? The results demonstrate robust generalization for both inshore and offshore scenarios, making the framework suitable for real-time maritime surveillance. This work provides a practical framework for oriented ship detection in complex SAR environments, highlighting the potential of deep learning in remote sensing applications.Abstract Synthetic aperture radar (SAR) serves as a prominent remote sensing (RS) technology, permitting continuous maritime surveillance regardless of weather or time. Although deep learning-based detectors have achieved promising results in SAR imagery, the majority of current algorithms rely on axis-aligned bounding boxes, which are insufficient for accurately representing arbitrarily oriented ships, especially under speckle noise, complex coastal clutter, and real-time deployment constraints. To address this limitation, we propose a YOLO-based oriented ship detector (YOSDet). Specifically, a dynamic aggregation module (DAM) is incorporated into the backbone to enhance feature representation against non-stationary backscattering. An objective-guided detection head (OGDH) is developed to decouple classification and localization, complemented by a localization quality estimator (LQE) to calibrate classification confidence by mitigating the impact of scattering center shifts. Comparative evaluations conducted on three public SAR ship detection benchmarks validate the effectiveness of YOSDet. The proposed model outperforms existing detectors, achieving mAP scores of 96.8%, 88.5%, and 67.3% on the SSDD+, HRSID, and SRSDD-v1.0 datasets, respectively. Furthermore, the consistency of our approach in both nearshore and offshore environments is confirmed through rigorous quantitative and qualitative assessments.

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