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<p>Maritime target recognition and image perception enhancement are gradually being promoted and applied in ocean engineering. This paper proposes the attentional multi-pixel fusion (AMF) algorithm for the intelligent navigation of unmanned surface vessels (USVs). The algorithm preprocesses the image pixel matrix in blocks, computes the mapping between regional and full-pixel matrices, and adaptively equalizes the mapping weights via a Gaussian-fuzzy matrix. This approach guarantees the preservation of the target contour and texture information. Compared with five classic enhancement algorithms, the AMF algorithm improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Experimental validation via YOLOv8 for maritime target detection demonstrates 2.1% and 2.4% improvements in the evaluation indices over training on 4000 original images, with shorter training times and lower confusion rates. In maritime target ranging, the AMF algorithm, coupled with the ISR method, exhibits the lowest improved stereo ranging mean absolute error and standard deviation values and higher similarity between the regional and full-pixel matrices. In summary, the AMF algorithm excels in target detection and ranging, offering promising applications in ocean engineering, such as marine resource exploitation, path planning, and intelligent collaboration among unmanned vessels.</p>

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Sea-Surface Target Enhanced Detection Based on Attentional Multi-Pixel Fusion

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Bo Shi,
  • Zi-tao Wang,
  • Lin Chen,
  • Tian-yu Cao,
  • Qi-qi Ge

摘要

Maritime target recognition and image perception enhancement are gradually being promoted and applied in ocean engineering. This paper proposes the attentional multi-pixel fusion (AMF) algorithm for the intelligent navigation of unmanned surface vessels (USVs). The algorithm preprocesses the image pixel matrix in blocks, computes the mapping between regional and full-pixel matrices, and adaptively equalizes the mapping weights via a Gaussian-fuzzy matrix. This approach guarantees the preservation of the target contour and texture information. Compared with five classic enhancement algorithms, the AMF algorithm improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Experimental validation via YOLOv8 for maritime target detection demonstrates 2.1% and 2.4% improvements in the evaluation indices over training on 4000 original images, with shorter training times and lower confusion rates. In maritime target ranging, the AMF algorithm, coupled with the ISR method, exhibits the lowest improved stereo ranging mean absolute error and standard deviation values and higher similarity between the regional and full-pixel matrices. In summary, the AMF algorithm excels in target detection and ranging, offering promising applications in ocean engineering, such as marine resource exploitation, path planning, and intelligent collaboration among unmanned vessels.