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Underwater litter detection is a crucial technology for maintaining the balance of underwater ecosystems. To address the challenge of significant variations in target scales encountered in underwater litter detection, we propose the YOLO11-MDA based on YOLO11 is proposed. Firstly, a multidomain feature extraction module MFEM is proposed, which is capable of extracting different scales of features from the input feature map by extracting the target features in both spatial and frequency domains, and enhances the ability of expression of the global features and local information. Second, the lightweight dynamic up-sampling DySample module is introduced to integrate contextual information and improve the quality and efficiency of up-sampling. Finally, the adaptive threshold focused classification loss ATFL is introduced to reduce the impact of the uneven distribution of multi-scale samples on the detection results and improve the detection accuracy of multi-scale targets. The experimental results show that compared with the baseline model, the mAP of YOLO11-MDA in TrashCan dataset and Trash_ICRA19 dataset reaches 91.4% and 97% respectively, which is an enhancement of 3.1% and 10.7%, and the FPS reaches the detection speed of 354.3 fps, which fully demonstrates that the overall performance of the improved model outperforms that of other algorithms, and it can provide an effective method for the automated monitoring of underwater environments.

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水下垃圾检测是维持水下生态系统平衡的关键技术。针对水下垃圾检测面临着目标尺度差异大的问题，提出一种基于YOLO11的水下垃圾检测算法YOLO11-MDA。首先，提出了一种多域特征提取模块MFEM，通过提取空域和频域的目标特征，能够从输入特征图中提取不同尺度特征，增强全局特征与局部信息的表达能力。其次，引入轻量级动态上采样DySample模块，融合上下文信息，提升上采样的质量和效率。最后，引入自适应阈值焦点分类损失ATFL，降低多尺度样本分布不均衡对检测结果的影响，提高多尺度目标的检测精度。实验结果表明，相比基线模型，YOLO11-MDA在TrashCan数据集和Trash_ICRA19数据集的mAP分别达到了91.4%和97%，提升3.1%和10.7%，FPS达到了354.3 fps的检测速度，充分说明改进的模型整体性能优于其他算法，为水下环境的自动化监测提供一种有效的解决方案。

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任艺，硕士研究生，主要研究方向为目标检测。E-mail: ry1838843470@163.com

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任艺，硕士研究生，主要研究方向为目标检测。E-mail: ry1838843470@163.com

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仲兆满，博士，主要研究方向为互联网舆情监测、多模态情感分析。E-mail: zhongzhaoman@163.com

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仲兆满，博士，主要研究方向为互联网舆情监测、多模态情感分析。E-mail: zhongzhaoman@163.com

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仲晓敏，硕士，主要研究方向为软件缺陷预测。E-mail: zhongxm@jou.edu.cn

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仲晓敏，硕士，主要研究方向为软件缺陷预测。E-mail: zhongxm@jou.edu.cn

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