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Hydroelectric dams have a vital role to play in renewable energy and water resources, and ensuring the safety and structural integrity of these dams is crucial. Cracks are a major disease hazard threatening the safety of dams, usually originating on the surface of the dam and continuously extending inwards under the action of hydraulic splitting. Timely and accurately detecting and identifying dam surface cracks is important for maintaining dam safety. However, due to the complexity of the underwater environment and the diversity of dam surface cracks, manual observation methods are time-consuming and laborious to classify underwater surface crack images of dams, and the existing methods have low recognition accuracy when used for the crack classification task. For this reason, this paper proposes an algorithm for classifying underwater surface cracks in hydroelectric dams, which classifies the cracks extracted from the image segmentation model of underwater cracks in hydroelectric dams with the help of an improved RepVGG network. A CBAM attention mechanism module is inserted into the RepVGG feature extraction network to extract a more effective feature representation. Due to the imbalance problem of data samples, the class balancing loss function of IB Loss is introduced to achieve class balancing. The experimental results show that for different kinds of cracks, the accuracy of the constructed algorithms is higher than the classical image classification algorithms, and the average classification accuracy of the network is improved by 2.06% compared to the original RepVGG algorithm, which is improved compared to all other traditional classification networks.
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