Automated crack classification for the CERN underground tunnel infrastructure using deep learning

Abstract

One early sign of tunnel structure deterioration originates in the form of cracking, and therefore crack detection and resultant classification is integral for tunnel structural inspection and maintenance. Conventionally tunnel cracks are manually recorded and classified by trained professionals, which is costly, time-consuming and inevitably subjective. Recent advances in deep learning space have allowed for automatic cracks detection algorithms to be developed and subsequently utilized in structural health assessment of surface buildings, bridges, roads and other civil infrastructure. Nevertheless, these methods of development underperform when implemented for a tunnel structure in an underground environment due to the disparity of illumination combined with the congested image data caused by pipes, steel mesh, wires, and other tunnel facilities. To overcome these challenges, this paper proposes an innovative image-based crack detection method accompanied with crack classification using Convolution Neural Network (CNN) specifically for underground infrastructure environment. Unlike conventional CNN development from scratch, the proposed CNN incorporates Transfer Learning in the form of the VGG16 model with weights transferred from ImageNet. The transfer model was trained under different scenarios in order to find the optimal model for the novel dataset. The various models are trained using over 10′000 images validated on 2′500 images all of which are 256 × 256 pixels in size, these models are all subsequently tested using 30 images 3072 × 4096 pixels in size all of which are collected from the underground infrastructure at CERN. The Transfer Learning model used outperforms that of the traditional CNN training method of training from scratch. The optimum transfer model accomplished better testing metrics 96.6%,87.3%,92.4%,89.3% for Accuracy, Precision, Recall and F1 score respectively all of which were achieved with a shorter training time. The proposed CNN determines the existence and location of cracks within an image which are then subjected to a secondary classification CNN where the crack is categorized into one of the four crack classes which include the three directional classes of Horizontal, vertical and diagonal with the last crack classes incorporated to represent complex crack regions. The secondary classification CNN attains an Accuracy of 92.3% a Precision of 83.9% a Recall value of 82.3 % and a final F1 score of 81.5%. The performance of the proposed methods shows that a CNN crack detector/classifier can effectively overwhelm the unfavourable tunnel environment and accomplish results to a high standard.