A self-supervised learning-based method for tunnel leakage defect few-shot recognition and visual explanation

The frequently used supervised learning (SL) methods for detecting tunnel leakage defect require a large amount of labeled data, which is costly and challenging for practical applications. Moreover, benchmarking model performance faces challenge of the inconsistent annotation standards of dataset. To address these limitations, based on the self-established tunnel leakage instance segmentation dataset, this study integrates self-supervised learning with YOLO11n model to facilitate few-shot leakage recognition. In the workflow, YOLO-SimSiam is constructed and pre-trained using unlabeled images to enhance leakage representation learning, and a comprehensive evaluation method is proposed to assess feature extraction performance. Subsequently, YOLO11n model with pre-trained weights are fine-tuned on a limited number of labeled images for precise leakage recognition through transfer learning. Experiment results show that the model achieves competitive performance with only 10% dataset (0.910) comparable to that of SL on entire dataset (0.930). Besides, a theoretical conversion formula between leakage pixel number and real area is deduced and verified in a field calibration experiment with an error rate of 1.759%, providing a normative defect severity evaluation measure. The proposed method can effectively reduce the dependency on extensive data annotation, and thus promote the application of deep learning in practical tunnel maintenance.