Deep Convolutional Neural Networks for Road Crack Detection: Qualitative and Quantitative Comparisons

Abstract

Road crack detection is a crucial civil infrastructure inspection task. Road crack detection is generally performed by either certified inspectors or structural engineers. Nevertheless, this process is time-consuming and subjective. Deep convolutional neural networks (DCNNs) have demonstrated compelling results for image classification, but there are currently no comprehensive comparisons among them, in regard to road crack detection. Therefore, in this paper, we conduct extensive experiments to compare 30 state-of-the-art (SoTA) DCNNs for road crack detection: Each DCNN is trained on a training set; The best performing models are selected on the validation set; Their performance is further quantified on a test set with respect to six evaluation metrics: precision, recall, accuracy, F-score, area under receiver operating characteristic (AUROC), and runtime. The experimental results suggest that road crack detection is a relatively easy image classification task. All the SoTA DCNNs perform similarly. The DCNNs evaluated in this study also achieved very similar performance when only a small amount of training data is available. Furthermore, PNASNet achieved the best trade-off between speed and accuracy, and thus, it is more practical to be used for real-time and robust road crack detection. Moreover, it was found that the best DCNN models did not generalize well when tested on new unseen data sets consisting of images not specifically related to road cracks.