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<p>Underwater robotic systems face significant challenges in object detection due to the complexity of marine environments. To address these challenges, our previous work introduced EAW-YOLO11, an enhanced object detection network based on the YOLO11 architecture and specifically designed for underwater scenarios. In this model, we propose two novel modules: the EC3k2 module with Efficient Multi-scale Attention (EMA) for improved feature extraction and the C2AIFI module for effective feature integration. In addition, we adopt the Wise-IoU v3 loss function to enhance localization performance. In this extended study, we further refine EAW-YOLO11 to address the overfitting issues observed in the initial version, specifically adjusting the momentum parameter during training. Experimental results on the URPC2019 dataset show that EAW-YOLO11 achieves a 2.1% increase in mAP@0.5 compared to the baseline YOLO11, demonstrating strong performance even in blurred and low-visibility conditions. Further ablation studies and qualitative evaluations confirm that EAW-YOLO11 is a promising solution for real-world underwater robotic applications, including marine exploration and autonomous navigation. The code will be released at <a href="https://github.com/successdang99/EAW-YOLO11">https://github.com/successdang99/EAW-YOLO11</a>.</p>

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EAW-YOLO11: enhanced YOLO11 network for underwater object detection

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • Cong Thanh Dang,
  • Hiroshi Sato,
  • Masao Kubo

摘要

Underwater robotic systems face significant challenges in object detection due to the complexity of marine environments. To address these challenges, our previous work introduced EAW-YOLO11, an enhanced object detection network based on the YOLO11 architecture and specifically designed for underwater scenarios. In this model, we propose two novel modules: the EC3k2 module with Efficient Multi-scale Attention (EMA) for improved feature extraction and the C2AIFI module for effective feature integration. In addition, we adopt the Wise-IoU v3 loss function to enhance localization performance. In this extended study, we further refine EAW-YOLO11 to address the overfitting issues observed in the initial version, specifically adjusting the momentum parameter during training. Experimental results on the URPC2019 dataset show that EAW-YOLO11 achieves a 2.1% increase in mAP@0.5 compared to the baseline YOLO11, demonstrating strong performance even in blurred and low-visibility conditions. Further ablation studies and qualitative evaluations confirm that EAW-YOLO11 is a promising solution for real-world underwater robotic applications, including marine exploration and autonomous navigation. The code will be released at https://github.com/successdang99/EAW-YOLO11.