Semi-supervised learning framework for crack segmentation based on contrastive learning and cross pseudo supervision

Abstract

Fast and accurate crack segmentation plays an important role in the predictive maintenance of constructed facilities and civil infrastructures. However, it is worth noting that current deep-learning-based algorithms for crack segmentation may face significant challenges due to the requirement of a large amount of labeled data for high-precision segmentation. A novel semi-supervised learning framework for crack segmentation, which is referred to as semi-supervised crack (SemiCrack), based on the combination of contrastive learning and cross pseudo supervision (CPS) is presented in this study. The proposed segmentation network, called transformer and convolutional network (TC-Net), has a novel parallel encoder that fuses a transformer and a convolutional neural network. The inclusion of CPS can force the two models to maintain consistent outputs for various perturbed data based on the similarity loss. To capture the feature differences between positive and negative sample pairs extracted by the classifier and projector, pixel contrastive loss was also proposed. Compared with many state-of-the-art fully-supervised and semi-supervised segmentation algorithms, the results show that SemiCrack performs best on various publicly available datasets. The segmentation accuracy of TC-Net is higher than that of most fully-supervised segmentation networks, with an improvement of about 2% in Intersection of Union (IoU). Besides, SemiCrack requires only 20% labeled data to achieve comparable accuracy to other fully-supervised algorithms that require 100% labeled data. When the amount of labeled data is small, the IoUs of SemiCrack are significantly improved compared to fully supervised and semi-supervised networks.