## 1 Introduction
Their algorithm applied Gaussian filtering and adaptive median filtering to remove detected Gaussian noise and salt-and-pepper noise, respectively, which effectively addressed the issue of poor noise filtering. [PERSON] et al. [13] used full cross-sectional images of tunnel surfaces collected by a multiline scanning camera. They applied a multistage fusion filtering algorithm with local threshold binarization to filter out background noise and interference from images of tunnel-surface water stains and various facilities. Moreover, they employed an improved threshold segmentation algorithm based on edge gradient information to segment real cracks from noisy backgrounds and applied image differencing and region search algorithms to defect detection. [PERSON] et al. [14] proposed a metro tunnel lining crack segmentation algorithm that combines adaptive threshold segmentation, edge detection, and dual-threshold Otsu methods. The algorithm merges the segmented cracks with the background regions and sequentially stitches the sub-blocks together to obtain the overall crack image. In summary, the use of digital image processing methods to extract apparent defects in tunnel linings involves calculations based on the pixel matrix of the original image. By designing filtering algorithms to remove known types of background noise and performing calculations based on the geometric or pixel characteristics of the target defects, good detection results can be achieved for tunnel image data with low complexity or linear feature background noise. However, in practice, metro tunnels have numerous facilities and similar textures, with adverse factors, such as uneven lighting, varying scales, and occlusions, leading to a large amount of nonlinear noise. For complex scenes and low-quality image data, the detection accuracy, efficiency, and robustness of these methods are limited. Additionally, these methods are constrained by the extraction of a single type of defect. When the original image contains multiple types of defects, the segmentation threshold and algorithm design must be adjusted accordingly, and the robustness and generalizability of the algorithms should be improved. In recent years, with the rapid development of deep learning in computer vision technology [15], many scholars have constructed deep learning-based object detection and semantic segmentation models to extract and integrate the multiscale features of defects, achieving the accurate detection and extraction of various apparent defects in metro tunnel linings [16]. [PERSON] et al. [17] built an FCN to quickly classify apparent defects, such as cracks and leakage, in images of metro tunnel linings and used the region-based FCN model to extract and classify the locations of defects in images containing them. [PERSON] et al. [18] proposed an algorithm combining faster regions with convolutional neural networks (Faster R-CNN), adaptive region-of-interest layers, and fully convolutional networks (FCNs) for detecting tunnel defects, such as cracks, leakage, and exfoliation, in metro tunnel linings. [PERSON] et al. [19] proposed a deep convolutional neural network (DCNN)-based model to achieve multichannel feature extraction of spalling defects in subway tunnel lining. The depth information helps to quantify the volume of spalling defects in the tunnel lining. [PERSON] et al. [20] enhanced the resolution of metro tunnel images via image enhancement operations, such as contrast enhancement, and used the Faster R-CNN detection model to achieve high-precision automatic detection of tunnel surface defects. [PERSON] et al. [21] also proposed a metro shield tunnel leakage detection method integrating cylindrical voxels and Mask R-CNN, achieving leakage detection and 3D visualization in different tunnel scenarios. Constructing deep learning networks evidently allows for targeted extraction of the deep features of apparent tunnel defects. This approach is more beneficial for the automatic detection, extraction, and classification of multiple types of defects.