## 1 Introduction
Compared with digital image processing techniques, deep learning networks offer better performance in large-scale and noise-complex metro tunnel defect detection tasks. However, most current research focuses on optimizing the performance of models themselves to improve defect detection accuracy. Model design thinking that integrates the distribution characteristics of tunnel defects and real-world detection scenarios is generally lacking. For practical engineering requirements, the extraction accuracy for specific defects still requires further improvement. In particular, current research faces the following challenges: (1) In actual metro tunnel scenarios, defects such as cracks, exfoliation, and leakage are mainly distributed around relatively weak areas such as lining joints, circumferential seams, and areas around bolt holes and grouting holes (Figure 1). Current research insufficiently focuses on these critical features. Fully leveraging the spatial location characteristics of defects is crucial for improving detection accuracy. (2) Small target defects on metro tunnel lining surfaces are not prominent and are difficult to detect, which limits detection accuracy. Enhancing the detection of small target defects remains a pressing research challenge. (3) Apparent cracks in metro tunnel linings, as early indicators of various defects, have received extensive attention in engineering practice. Currently, the standard for detection accuracy has reached the millimeter level, highlighting the importance of high-precision extraction. However, given the undistinguishable features in the early stages of crack development and limitations in sensor performance, defect areas often suffer from blurred details and low image quality. These factors pose significant challenges for the accurate and high-precision extraction of cracks. In this study, an algorithm for detecting apparent defects in tunnel linings and associated facilities in metro tunnel scenarios is proposed to address the aforementioned issues. Additionally, image super-resolution models and transfer learning techniques are used to enhance the resolution and clarity of the detected crack areas. In particular, the main contributions of this study are as follows:(1) By analyzing the characteristics of metro tunnel detection scenarios and the lining appearance data, YOLOv8, a single-stage object detection model, was selected as the network architecture. Then, the CA was introduced into the backbone of the model to fully utilize the spatial location features of defects and associated facilities. This approach embeds positional information into channel attention to capture the target structure, enabling the network model to focus efficiently on defect-prone areas and assign higher weights to hotspots. Furthermore, a transformer computation module was incorporated into the model by using Bottleneck Transformer 3 (BoT3) to extract global contextual information from defect images. This module models the global feature dependency relationships of the feature map, learning the feature associations between defect-prone areas, their background information, and distant targets. This approach helps the model better understand the global scene and defect occurrence features, thereby enhancing the extraction of associative information between apparent defect locations, their background areas, and related facilities. (2) A shallow, high-resolution detection layer was added to the model to address the issues of small-scale, undistinguishable features of minor defects in metro tunnel linings. This layer uses clearer positional and structural information about the defects to precisely locate small target defects and their structures. The detection accuracy of apparent defects in metro tunnel linings was improved by the abovementioned integrated optimization strategies.