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By integrating the CBAM(Convolutional Block Attention Module) attention mechanism into the C3 structure of YOLOv5s, the ability to capture subtle features of underwater debris has been significantly enhanced. Additionally, GhostConv lightweight convolution layers have been introduced into the model\u2019s neck network, which not only accelerates the computational speed but also ensures the stability of feature extraction. Experiments show that the proposed algorithm achieves a recognition accuracy of 88.0% on a self-built underwater garbage dataset with an average detection time of just 6.8\u00a0ms. This improved model surpasses both YOLOv5s and similar algorithms in recognition accuracy and operational efficiency. 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