###### Abstract
At a working distance of six meters, it has a resolution of 0.2 mm and an operating speed of 2-5 km/h, which can be applied to the health status detection of subways, highways, and railway tunnels [10]. [PERSON] et al. developed a prototype device of a vehicle-mounted automatic detection system for tunnel linings composed of linear array CCD cameras and LED light sources. Through the experiment of the crack acquisition model, the influence rules of the detection distance, light source illumination, effective pixels, and detection speed on the automatic detection performance are established [11]. [PERSON] et al. designed a 360\({}^{\circ}\) panoramic camera composed of a camera and a hyperboloid catadioptric mirror. Fifteen LED light sources are used to fill in the light for tunnel lining image acquisition, which can obtain the tunnel lining panorama at one time. Because of catadioptric technology, the image clarity is the same in any region, which does not need to focus anymore [12]. The tunnel inspection vehicle TFS [13] developed by Wuhan ZOYON Company and the tunnel inspection vehicle TDV-H2000 [14] of Shanghai TJGEO Technology Company both carry out mobile monitoring in the tunnel by integrating cameras and laser scanning systems on the vehicle, whose crack detection accuracy can reach 0.2 mm. However, there is no introduction to its imaging system's selection, parameter calculation method, or image characteristics. [PERSON] et al. established the optimization model of the multi-camera installation center and the single-camera installation position, which gave the optimal design results of the installation parameters of the vision acquisition system. Three line scan cameras with 4 K resolution are integrated on the Stewart platform with rotation and vibration reduction functions, which can achieve mm-level resolution in the tunnel [15,16,17]. [PERSON] et al. developed an MTI-100 tunnel inspection vehicle for subway tunnel inspection. It consists of three systems: image acquisition system, control system, and moving control system. Through human push, the tunnel lining image acquisition can be carried out at a speed of 3-5 km per hour in the subway tunnel. The system consists of six line scan cameras with a resolution of 0.3 mm/pixel. Since the acquisition is carried out in a circular shield tunnel with a relatively fixed size, it is not necessary to adjust the shooting parameters frequently [18]. [PERSON] et al. proposed an aerial solution for visual inspection of the tunnel. Four vision cameras and one thermal imager are installed on the DJI Matrice100 quadrotor, and the flight speed can reach 2 m/s. At a shooting distance of two meters, the minimum pixel resolution can reach 0.86 mm/pixel. The solution using aircraft can be used in subways and road tunnels [19]. The existing research mainly focuses on developing the hardware of the lining image shooting system, the integrated application of the system, and the comparison of the detection efficiency. However, there is little research on the calculation of shooting parameters. Most systems require manual adjustment of shooting parameters during the detection process [20]. On the one hand, the operation time of the detection system is increased, and on the other hand, operators have to adjust parameters in the dangerous environment of on-service tunnels for a long time, which has a great negative impact on the imaging quality and shooting efficiency. Furthermore, the analysis of the obtained crack images is also very limited, which makes it difficult to extract information such as crack width without the aid of auxiliary equipment, e.g., calibration plates. Our main contributions can be summarized as follows: Firstly, by studying the image acquisition system of tunnel lining cracks, the hardware system selection is formed, and the calculation methods of three parameters are established. Then, the crack characteristics of tunnel lining images are analyzed, and the crack width extraction model and the optimal gray value for crack extraction are established.