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For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the backbone to improve the training and inference capabilities. The Efficient Intersection over Union (EIoU) loss is also used as the localization loss function, which reduces the error detection rate and missed detection rate of the algorithm. The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition) dataset show that the mAP(mean Average Precision) score of the algorithm is 86.1%, which is a 2.2% improvement compared to the YOLOv7. The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments, and it is more suitable for underwater target detection.

An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7