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<p>In underwater image analysis, challenges such as complex environments, low model performance, and slow processing efficiency hinder effective object detection, which is crucial for real-time monitoring. To address these issues, we propose a high-precision underwater object detection model named YOLO-LDFE. To overcome the limitations of existing datasets, we have developed a comprehensive multi-species underwater biological dataset, MCUA, containing 9327 labeled images across 14 categories. The primary contributions of this work are as follows: (1) SPPF_SCSA, which integrates multi-semantic information with channel-space attention mechanisms to enhance performance; (2) the substitution of traditional convolutions with GSConv in C2f, reducing model size while maintaining feature extraction; (3) LEDF, which improves performance through multi-level, dense connections. YOLO-LDFE achieves exceptional results, with an average precision of 93% on the URPC2021 dataset, outperforming existing algorithms while maintaining high detection speed, demonstrating its potential for real-time underwater monitoring.</p>

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Enhanced underwater object detection with YOLO-LDFE: a model for improved accuracy with balanced efficiency

  • 期刊论文

  • 发表日期：

    2025年2月10日

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  • JiaXin Liu,
  • RiGui Zhou,
  • YaoChong Li,
  • PengJu Ren

摘要

In underwater image analysis, challenges such as complex environments, low model performance, and slow processing efficiency hinder effective object detection, which is crucial for real-time monitoring. To address these issues, we propose a high-precision underwater object detection model named YOLO-LDFE. To overcome the limitations of existing datasets, we have developed a comprehensive multi-species underwater biological dataset, MCUA, containing 9327 labeled images across 14 categories. The primary contributions of this work are as follows: (1) SPPF_SCSA, which integrates multi-semantic information with channel-space attention mechanisms to enhance performance; (2) the substitution of traditional convolutions with GSConv in C2f, reducing model size while maintaining feature extraction; (3) LEDF, which improves performance through multi-level, dense connections. YOLO-LDFE achieves exceptional results, with an average precision of 93% on the URPC2021 dataset, outperforming existing algorithms while maintaining high detection speed, demonstrating its potential for real-time underwater monitoring.