A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network

The problem of the underwater structure disease of the bridge is increasingly obvious, which has seriously affected the safe operation of the bridge structure, so it is necessary to detect the underwater structure regularly. There are many kinds of bridge underwater structure diseases. This paper targets the bridge underwater structural crack diseases adopts multiple image recognition networks for verification, compares the advantages of different networks, and takes the YOLO‐v4 network as the main body to build a lightweight convolutional neural network.Mobilenetv3 replaced CSPDarkent as the backbone feature extraction network, while the feature layer scale of Mobilenetv3 was modified, and the extracted preliminary feature layer was input into the enhanced feature extraction network for feature fusion. The PANet networks are replaced by the depthwise separable convolution. Using ablation experiments to compare the performance of four algorithm combinations in lightweight networks. At the same time, the disease identification accuracy of each network and the performance of the network are tested in various experimental environments, and the feasibility of the lightweight network is verified in the application of bridge underwater structure damage identification.