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In order to achieve rapid and accurate identification of salmon in complex underwater environments, a lightweight salmon detection model, YOLO v7-CSMRep, was proposed based on YOLO v7. Firstly, by adopting the Stem module, the first four convolutional operations in the backbone layer were merged into an efficient convolutional operation, reducing the computational load of the model. Secondly, the ELAN and ELAN-H modules of the YOLO v7 network were replaced with the multi-directional reparameterization (MRep) module, which enhanced the one-way feature extraction capability while greatly reducing parameters and calculations. Finally, at the end of the backbone layer, the convolutional block attention module (CBAM) was integrated to enhance the network’s spatial and channel feature extraction capabilities. The experimental results showed that the improved model’s volume, parameter count, and computational load were reduced by 4.28%, 5.29% and 31.30%, respectively. The F1 score and mAP0.5 were increased by 0.5 and 0.7 percentage points, and reached 93.1% and 97.1%, respectively. Additionally, the frame rate was increased by 15.41%, and reached 140.8f/s. Compared with that of YOLO v5s, YOLO v6s, YOLO v7, YOLO v7-tiny, and YOLO v8s models, the mAP0.5 was improved by 1.0, 2.0, 0.7, 0.8, and 1.2 percentage points, respectively. Therefore, the method proposed can rapidly and accurately identify salmon and provide technical support for biomass monitoring in deep-sea aquaculture.