Research on a Crack Extraction Algorithm of Bridge Deck of Simple Supported Girder

The crack detection of the simple support beam bridge before leaving the factory is an extremely critical step in its production and casting process, which is directly related to whether the bridge can be serviced normally. In this paper, an improved bridge deck crack extraction algorithm based on ResNet and Fcn convolutional neural networks is proposed. Firstly, a 1-3-1 mode residual network is constructed based on ResNet network to extract the features of the input crack pictures. Then, the size restoration work is carried out by the deconvolution module, and a multi-scale information fusion module is added to increase the receptive field to ensure the transmission of detailed information. Experiments show that the average pixel accuracy of the proposed algorithm is 0.84 and the average intersection-union ratio is 0.81, which is better than other comparison algorithms, and can effectively complete the task of crack detection of the bridge deck of simple support girder.