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FSP-YOLO: A Lightweight Algorithm for Ship Detection in SAR Images Under Complex Backgrounds
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DOI：10.1109/access.2026.3719648.png)
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Abstract
En 中文
Synthetic aperture radar (SAR) is indispensable for maritime monitoring due to its all-weather, high-resolution imaging capabilities. However, inherent speckle noise, complex inshore sea clutter, and limited computational resources of edge devices prevent existing algorithms from balancing high accuracy with lightweight deployment. To address these challenges, an efficient, lightweight model termed FSP-YOLO was developed for complex backgrounds. First, a Frequency-Spatial Attention Module (FSAM) was designed; it incorporates a two-dimensional discrete cosine transform (2D-DCT) to decouple high-frequency target features from low-frequency redundant noise, precisely focusing on strong scattering centers. Second, a lightweight multi-scale feature fusion neck network utilizing Partial Convolution (PConv), designated as C3-PConv, was constructed to eliminate computational redundancy while preserving cross-scale representational capacity. Finally, the Minimum Point Distance Intersection over Union (MPDIoU) loss function optimized bounding box regression by minimizing corner geometric distances, significantly improving localization accuracy for densely clustered and weak targets. Extensive evaluations on the standard SAR Ship Detection Dataset (SSDD) and the High-Resolution SAR Images Dataset (HRSID) demonstrated that FSP-YOLO effectively alleviated missed detections under complex interference conditions. Compared with the YOLOv11n baseline, FSP-YOLO reduced the parameter count and computational complexity by 15.4% and 7.6%, respectively, resulting in only 2.2M parameters and 6.1 GFLOPs, while achieving mAP@50 scores of 98.3% on SSDD and 92.3% on HRSID. In addition, the proposed method achieved an inference speed of 118.2 FPS on an RTX 3090 GPU. These results indicate that FSP-YOLO achieves a favorable balance among detection accuracy, model complexity, and real-time inference capability, making it a promising lightweight solution for practical maritime surveillance and edge deployment on resource-constrained platforms.
Keywords:
Attention mechanisms
deep learning
object detection
synthetic aperture radar
YOLO
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3.6
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