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<p>Marine debris detection plays a crucial role in environmental protection and intelligent underwater robotics. However, the underwater environment poses unique challenges due to low visibility, object occlusion, and high background noise. To address these issues, this paper proposes an improved object detection framework based on YOLOv11, enhanced by two novel modules: the MixStructureBlock and Efficient Multi-scale Attention (EMA). The MixStructureBlock integrates multi-scale dilated convolutions and hybrid attention mechanisms to strengthen the feature extraction capacity of the backbone, while EMA refines feature maps in the detection head through spatial-channel fusion and grouped attention. The model is trained and evaluated on the TrashCan-Instance and TrashCan-Material datasets. Experimental results demonstrate that the proposed method achieves superior detection accuracy, reaching mAP@0.5 of 81.54% and 82.75% on the two datasets respectively, outperforming baseline detectors such as YOLOv11, YOLOv8, YOLOTrashCan and further surpassing recent underwater-specific SOTA models including TC-YOLO and YOLOv8-MU, achieving the competitive and superior performance among the compared methods. Ablation studies further verify the individual contributions of each module. The proposed architecture achieves an excellent balance between accuracy and efficiency, making it suitable for real-time marine debris monitoring applications. Our code is available at: <a href="https://github.com/DavidJing777/yolo-marine">https://github.com/DavidJing777/yolo-marine</a>.</p>

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An improved YOLOv11 network for marine debris detection in underwater environment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

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  • Jing Yuanwei,
  • Ding Yijiang,
  • Wang Xuemei,
  • Anis Salwa Mohd Khairuddin

摘要

Marine debris detection plays a crucial role in environmental protection and intelligent underwater robotics. However, the underwater environment poses unique challenges due to low visibility, object occlusion, and high background noise. To address these issues, this paper proposes an improved object detection framework based on YOLOv11, enhanced by two novel modules: the MixStructureBlock and Efficient Multi-scale Attention (EMA). The MixStructureBlock integrates multi-scale dilated convolutions and hybrid attention mechanisms to strengthen the feature extraction capacity of the backbone, while EMA refines feature maps in the detection head through spatial-channel fusion and grouped attention. The model is trained and evaluated on the TrashCan-Instance and TrashCan-Material datasets. Experimental results demonstrate that the proposed method achieves superior detection accuracy, reaching mAP@0.5 of 81.54% and 82.75% on the two datasets respectively, outperforming baseline detectors such as YOLOv11, YOLOv8, YOLOTrashCan and further surpassing recent underwater-specific SOTA models including TC-YOLO and YOLOv8-MU, achieving the competitive and superior performance among the compared methods. Ablation studies further verify the individual contributions of each module. The proposed architecture achieves an excellent balance between accuracy and efficiency, making it suitable for real-time marine debris monitoring applications. Our code is available at: https://github.com/DavidJing777/yolo-marine.