## 1 Introduction  
With the continuous increase in urban metro operation time, early-constructed metro tunnels, under the coupled effects of geological conditions and operating environments, generally suffer from structural aging problems. These issues manifest as lining cracks, water leakage, and exfoliation [1]. The interaction between the apparent defects of the lining and its structure poses significant safety hazards for tunnel operations. When faced with extreme weather conditions and emergencies, such as heavy rain, changes in groundwater flow, damage to underground municipal facilities, and other underground engineering activities, metro tunnels can be impacted to varying degrees. The safety risks associated with lining defects are consequently amplified. These issues can exacerbate the degree of lining defects, increase the affected areas, and cause structural damage such as misalignment and voids. In severe cases, these problems can lead to catastrophic consequences such as tunnel flooding, collapse, and surface subsidence [2]. Therefore, timely detection of apparent defects in metro tunnels is extremely crucial for metro operation and public safety. In engineering practice, the detection of apparent defects in metro tunnels primarily relies on manual inspections and the use of mobile measurement equipment for data collection and manual identification. However, as the pace of urban life accelerates and metro operation times increase, the maintenance window period for tunnels is increasingly compressed. The inefficiency, low precision, and over-reliance on subjective judgment of these methods are becoming more apparent [3]. These issues have led to growing research interest in developing accurate and efficient automatic detection technologies for metro tunnel lining defects in the field of tunnel maintenance. Over the past few decades, the use of image-based techniques for tunnel damage detection has seen significant advancements. The introduction of image processing technologies in tunnel inspection has revolutionized the field by offering non-contact, accurate, and efficient means of detecting structural and apparent defects. In related research on the automated detection of lining defects, visible light cameras or LiDAR (Light Laser Detection and Ranging) are typically used to collect and process data, resulting in panoramic grayscale images of the tunnel lining as the raw data [4; 5; 6; 7; 8]. Depending on the detection environment and target features, methods such as digital image processing and deep learning are employed for defect detection and extraction. The detection of apparent tunnel defects by using digital image processing technology primarily relies on low-level features, such as pixel grayscale values, gradient changes, grayscale histograms, and frequency domain characteristics of the defects, so that they can be detected in the image. Algorithms such as image enhancement, noise reduction filtering, and threshold segmentation are used to extract image regions corresponding to different types of defects [9]. [PERSON] et al. [10] used frequency-shifted feedback (PSF) laser technology to achieve high-resolution imaging for the detection of apparent cracks in subway tunnel linings, capable of detecting cracks as narrow as 0.2 mm. [PERSON] et al. [11] used a differential minimization algorithm to perform image differencing on surface images of metro tunnel linings collected at different times from the same location. They employed a neighborhood-connected region search algorithm and classified apparent defects, such as leakage, exfoliation, and cracks, based on the geometric parameters of the connected regions and the characteristics of the defects. In other related studies, targeted filtering algorithms and threshold segmentation algorithms have received widespread attention. [PERSON] et al. [12] introduced an adaptive median-Gaussian filtering algorithm to further improve noise filtering in tunnel lining crack detection.