Research on Crack Identification of Highway Tunnel Linings Based on Data Obtained from the Testing Vehicle

With the continuous construction of highway tunnels, the problems of disease identification and maintenance of highway tunnels are increasing. The traditional tunnel lining crack identification requires a lot of personnel to participate in the indoor work, resulting in low efficiency. Based on the image data collected by the rapid testing vehicle for highway tunnel condition, this paper uses the convolutional neural networks to train a set of models for recognizing the cracks in the highway tunnel linings. According to the problems found in the training process, a comparative study on the effect of different crack data sets on the recognition effect of the neural network models is carried out, proving that the CNN model trained by images sorted by crack development trends has better recognition ability and better applicability.