Abstract

Marine ecosystem integrity is paramount to global stability. With the advancement of industrialization, various types of waste are discharged into the ocean, accumulating through the food chain and ultimately threatening human health and the global climate environment. To achieve precise and efficient cleanup of marine debris, traceability is essential, with detection and classification serving as critical steps. To address the issues of missed detection and occlusion caused by the irregular shapes of marine debris due to water pressure or structural characteristics, as well as the coexistence of multi-scale objects resulting from aggregation and shooting angles, this study proposes the MD-YOLO model based on the YOLOv11L architecture. Firstly, a deformable attention mechanism is introduced in the neck network to achieve dynamic sampling and precise localization of targets with imbalanced aspect ratios. Secondly, a context-aware multi-scale feature fusion module is embedded in the backbone network to effectively mitigate the issue of missed detection of small targets when objects of different sizes coexist. Finally, a cooperative spatial-channel attention mechanism is designed in the detection head to enhance the feature representation capability in visible regions and infer occluded areas, thereby significantly suppressing occlusion interference. Experiments conducted on a self-constructed dataset containing 5095 images demonstrate that the proposed method achieves 86.7% in mAP@0.5, 67.6% in mAP@0.5:0.95, and an F1 score of 0.83, significantly outperforming comparative methods. This study provides key technical support for the effective traceability of marine debris.

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