Semi-supervised learning for efficient water leakage segmentation in tunnel infrastructure

Water leakage segmentation based on computer vision allows for efficient monitoring and maintenance, ensuring the safety and integrity of tunnel infrastructure. In this study, a semisupervised learning method with self-training via pseudo-labels is proposed to overcome the dependence of conventional computer vision models on high-quality labeled data. Strong data augmentation is first injected into the unlabeled data to establish a robust self-training baseline and differentiate similar predictions between teacher and student networks. To mitigate the accumulation of erroneous pseudo-labels and their potential impact on performance, we chose to retrain using only reliable, unlabeled images. The unlabeled images are prioritized based on their overall stability. By considering image-level contextual information, the selection process offers a more suitable approach for segmentation compared to conventional pixelwise methods. The experimental results highlight the method’s capability to enhance model performance with a limited amount of labeled data, surpassing the effectiveness of other semisupervised methods.