| Summary: | For the task of detecting marine targets, numerous machine-learning methods have been suggested, which can achieve comparable accuracy. Despite the successful application of deep learning in the field of marine target detection in recent years, existing detection methods face challenges due to the significant interference of sea clutter. As artificial intelligence technology advances, the Swin Transformer can serve as an effective backbone for extracting discriminative features. However, it has not yet been utilized for target detection, and the combination of Swin Transformer and YOLO architecture has not been applied to similar missions. In light of this, we propose a novel method, YOLO-SWFormer, which combines the Swin Transformer and YOLO framework for target detection. Our method can extract discriminative features from plan-position indicator (PPI) images despite the interference of sea clutter, thereby reducing computational complexity and enhancing target detection accuracy. Experimental results on a Sea Clutter Database demonstrates that our method surpasses existing methods in terms of accuracy, indicating its potential as a promising solution for marine target detection tasks.
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