Aiming at the problem of low underwater object detection accuracy caused by low-quality underwater imaging, different shapes or sizes of underwater objects, and overlapping or occlusion of underwater objects, an underwater object detection algorithm combining data enhancement and improved YOLOv4 is proposed. By adding CBAM ( convolutional block attention module) to the backbone of YOLOv4—CSPDarknet53, the feature extraction ability of network model is improved. In order to enhance the multi-scale feature fusion ability, the same-layer skip connections and cross-layer skip connections are added to PANet (path aggregation network). To enhance the robustness of the network model, the data enhancement method PredMix (prediction mix) is used to simulate the incomplete display of underwater organisms such as overlap or occlusion. The experimental results show that the detection accuracy of the underwater object detection algorithm combining data enhancement and improved YOLOv4 on URPC2018 dataset is improved to 78. 39%, 7. 03% higher than YOLOv4, which fully proves the effectiveness of the proposed algorithm.