Research on hybrid segmentation method and application of multi-category defect detection in tunnel linings based on deep learning

• Constructed the largest multi-category dataset for tunnel lining defect detection following rigorous data collection, analysis, and processing. • Proposed an improved KNet-based segmentation model that is validated to outperform multiple composite algorithms under a consistent transfer learning strategy. • Analyzed the model's decision-making process and performance factors in depth via progressive visual interpretability and multi-index joint analysis. • Developed a desktop application for the Central Yunnan Water Diversion Project to facilitate the efficient detection and identification of lining defects in experimental sections. During tunnel construction and operation, the timely detection and treatment of lining defects are critical to ensuring the safety and stability of tunnel engineering. To achieve fast and accurate detection of multi-category tunnel lining defects from image data, this paper proposes an improved model based on the KNet segmentation algorithm, integrated with the Swin Transformer and UPerNet models. First, a large-scale multi-category image dataset of tunnel lining defects is constructed by collecting and organizing data from relevant literature and public datasets. Second, the pre-trained KNet-enhanced model is trained on this dataset using transfer learning to improve detection accuracy and generalization performance. The proposed KNet-UPerNet-SwinL model achieves an average improvement of approximately 16.94% in mean Intersection over Union (mIoU) compared to other classical semantic segmentation methods, demonstrating superior segmentation accuracy and stability. In addition, an in-depth discussion of the model’s decision-making process and the factors influencing its performance is provided through visually interpretable progressive feature analysis and multi-index joint analysis at the application level. Finally, to facilitate practical application, the model is converted into the Open Neural Network Exchange (ONNX) universal format, and a desktop application for tunnel lining defect detection is developed. The detection and recognition of lining defects are successfully implemented in the experimental sections of the Central Yunnan Water Diversion Project, achieving remarkable segmentation results. In summary, this study provides a comprehensive solution that offers practical technical and tool support for multi-category defect detection in tunnel linings and holds significant importance for the safety and stability of tunnel engineering.