Image Recognition Algorithm of Complex Cracks in Metro Tunnel based on Neural Network

Abstract

With the rapid development of urban rail transit in my country, the safety inspection of subway tunnel infrastructure is in urgent need of automated and intelligent theory and technology research and development. The subway tunnel lining environment is bad, there are a lot of noise and uneven illumination, and the texture features are complex. The traditional manual and image detection and recognition methods have gradually been replaced by more intelligent and effective deep learning algorithms. Therefore, this paper proposes a subway tunnel lining image recognition and detection algorithm that combines image processing and deep convolutional networks, which can quickly and accurately distinguish between cracked images and non-cracked images, and can realize intelligent target search and category labeling in complex images. In this experiment, a total of 4000 original images of subway tunnels were selected, and pixel-level shallow processing operations such as batch preprocessing, hierarchical feature analysis, and extraction of the circumscribed rectangle of the connected area were carried out. For the training sample library of crack images, pixel-level image processing experiments and classification and recognition experiments are carried out for the training sample library to obtain the optimal database processing results, and then the image recognition algorithm for complex cracks in subway tunnels is studied. Experiments show that there are more point noises and block noises in the results of the subway tunnel crack images, and the segmentation results of the crack images have relatively less noise residues. Due to the wall material, some cement materials are a little off, resulting in noise retention. This shows that the algorithm in this paper has a certain application value in the intelligent identification of actual subway tunnel diseases, and provides technical support and research ideas for the application of subsequent deep learning in subway tunnel disease detection.