## 7 Conclusions  
To address the challenges of insufficient utilization of spatial location characteristics in detecting apparent defects in metro tunnel linings, limited detection accuracy (especially for small-scale defects), and poor detail clarity and geometric feature extraction accuracy for fine defects, this study proposes a comprehensive deep learning-based subway tunnel lining apparent-defect detection algorithm. The algorithm consists of two main components: (1) A detection model for apparent defects in metro tunnel linings. A YOLOv8-based framework was employed, incorporating the coordinate attention (CA) module, Bottleneck Transformer 3 (BoT3), and a high-resolution detection layer. These enhancements significantly improve the model's focus on defect-prone areas in metro tunnel linings and enhance detection accuracy, particularly for small-scale defects. The experimental results demonstrate that the improved detection model achieves a mean average precision (mAP) of 87% in identifying cracks, exfoliation, water leakage, and other apparent facilities, representing a 4.3% increase compared to the original YOLOv8 model. Moreover, its overall performance is better suited to metro tunnel detection scenarios compared to other object detection models. (2) A subway tunnel lining fine-crack enhancement model. In this component, a dataset of low-quality concrete cracks was constructed based on an open-access dataset, and theReal-ESRGAN model, combined with transfer learning techniques, was applied to enhance the resolution and clarity of fine-crack images. This approach addresses the challenges of limited detail and unclear texture in detected crack regions. The super-resolution techniques increased the spatial resolution of the crack images by four times, with PIQE and ENIQA values reaching 2.7 and 0.092, respectively, indicating a significant improvement in image quality. This enhancement provides robust data support for precise crack segmentation and geometric feature extraction tasks.  
The above results indicate that the proposed algorithm can provide technical support for metro tunnel defect detection and maintenance tasks. Its robust performance in detection accuracy and image enhancement makes it particularly well suited for application in the metro tunnel environment. The proposed algorithm could contribute to the advancement of intelligent metro tunnel maintenance systems.  
Conceptualization, A.Z. and J.Z.; methodology, A.Z., J.Z. and Y.C.; software, A.Z., S.Q. and D.W.; validation, S.Q., D.W. and Y.C.; formal analysis, J.Z.; investigation, A.Z. and S.Q.; resources, A.Z. and D.W.; data curation, A.Z. and S.Q.; writing--original draft preparation, A.Z. and Y.C.; writing--review and editing, A.Z., J.Z. and Y.C.; visualization, A.Z. and D.W.; supervision, J.Z.; project administration and funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shenzhen Science and Technology Innovation Commission (grant Nos. 20220810173255002 and JCYJ20230808105103006).  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.