YOLO-based marine organism detection using two-terminal attention mechanism and difficult-sample resampling

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The presence of various types of noise in images of marine-life datasets, as well as the class imbalances in underwater datasets, can exacerbate the difficulty in achieving effective object detection. To address this problem, we proposed you only look once (YOLO)-based marine organism detection using a two-terminal attention mechanism and difficult-sample resampling process. First, a residual building unit (RBU) module with a two-terminal attention mechanism (RBU-TA) was proposed, incorporating a reinforced channel attention mechanism into a shortcut of the residual structure. The proposed method adaptively compressed noisy feature map channels, providing rich shallow image information for high-level deep convolutional features while avoiding shallow noise pollution. To address the imbalance of marine biological image classes, difficult-sample resampling was combined with a focal loss function to suppress excessive background negative samples and retrain targets that could be difficult to distinguish, thus improving their detection accuracy. Finally, the proposed method was validated using the underwater robot professional competition (URPC) and real-world underwater object detection (RUOD) datasets, and the mean average precision (MAP) values of the results improved by 10% and 7%, respectively. The proposed method greatly improved the target detection accuracy of organisms in complex marine environments.