关键词: 水下目标检测, YOLO, 注意力机制, 特征融合, 归一化Wasserstein距离

Abstract: To address issues in underwater object detection, such as object features being easily overwhelmed by noise, insufficient multi-scale feature fusion accuracy, and poor adaptability of small target localization metrics, a underwater object detection algorithm named FEAR-YOLO based on YOLO11 architecture was proposed. First, a Channel Differentiated Convolutional Attention (CDCA) module was designed. Distinct spatial attention weights were dynamically generated for each channel to enhance the model’s ability to extract features from blurred and non-salient objects. Secondly, a Modulation Fusion Module (MFM) was proposed. By adaptively adjusting the fusion weights of shallow details and deep semantic features, the insufficient cross-level feature fusion in traditional methods was resolved, and the model’s sensitivity to key features of overlapping and occluded objects was improved. Finally, the Normalized Wasserstein Distance considering Shape and Scale (NWDSS) was introduced to replace the traditional Intersection over Union (IoU) metric. By measuring distribution differences to adapt to the scale characteristics of small targets, the problem of degraded localization accuracy caused by scale sensitivity was solved. Experimental results demonstrated that on the URPC2020 dataset, FEAR-YOLO achieved mAP@0.5 and mAP@0.5:0.95 of 75.8% and 44.0%, respectively, outperforming YOLO11L by 2.7 and 2.3 percentage points while containing only 25.6×106 parameters. In addition, the proposed method achieved substantial improvements across three underwater datasets (UDD, DUO and RUOD), verifying its effectiveness and generalization ability in complex underwater environments.

Key words: underwater object detection, YOLO, attention mechanism, feature fusion, normalized Wasserstein distance