End-to-end semi-supervised deep learning model for surface crack detection of infrastructures

Surface crack detection is essential for evaluating the safety and performance of civil infrastructures, and automated inspections are beneficial in providing objective results. Deep neural network-based segmentation methods have demonstrated promising potential in this purpose. However, the majority of these methods are fully supervised, requiring extensive manual labeling at pixel level, which is a vital but time-consuming and expensive task. In this paper, we propose a novel semi-supervised learning model for crack detection. The proposed model employs a modified U-Net, which has half the parameters of the original U-Net network to detect surface cracks. Comparison using 20 epochs shows that the modified U-Net network requires only 15% training time of the traditional U-net, but improves the accuracy by 20% upwards. On this basis, the proposed model (modified U-Net) is trained based on an updated strategy. At each stage, the trained model predicts and segments the unlabeled data images. The new strategy for updating the training datasets allows the model to be trained with limited labeled image data. To evaluate the performance of the proposed method, comprehensive image datasets consisting of the DeepCrack, Crack500 datasets those open to public, and an expanded dataset containing 2068 images of concrete bridge surface crack with our independent manual labels, are used to train and test the proposed method. Results show that the proposed semi-supervised learning method achieved quite approaching accuracies to the established fully supervised models using multiple accuracy indexes, however, the requirement for the labeled data reduces to 40%.