Abstract: Bridge safety is one of the most important parts of traffic safety. Bridge inspection is the most effective evaluation method for bridge safety, and the identification of diseases is especially important in bridge inspection. According to relevant statistics, more than 80% of the diseases of concrete bridges in China are caused by cracks. Therefore, the health inspection of bridges is mainly the detection and measurement of the apparent cracks of bridges. In order to improve the accuracy and efficiency of detection, and to count the accuracy and convergence of bridge crack disease images, this paper combines Convolutional Neural Networks (CNN) technology and uses bilinear interpolation provided by TensorFlow to preprocess crack images. We preprocess crack images into resolutions of 75 px, 150 px and 300 px; and build a CNN training model in the TensorFlow architecture, use this model to train horizontal crack disease pictures with resolutions of 75 px, 150 px and 300 px respectively, and analyze at different resolutions under the circumstances, the training accuracy, convergence and efficiency of the lateral crack disease. The three different resolutions of the transverse crack disease picture training result model are saved as the test model of the lateral crack disease identification, and these models are used to test the lateral crack picture in the actual engineering application and the lateral crack picture in the test set for analyzing the recognition accuracy, convergence and efficiency of the test set pictures after preprocessing and the transverse crack pictures taken in the actual environment. Experimental results show that compared with the recognition algorithm used by Liu Hong-gong et al, the training accuracy, scalability and real-time performance of the algorithm in this paper are better, and the recognition rate is as high as 99% and above, which lays a good foundation for the recognition of other crack diseases. The foundation can also provide reference data for the detection of bridge cracks.

Key words: lateral crack, graphic image recognition, convolutional neural network, convergence