## 6 Discussion  
A tunnel inspection vehicle that can quickly carry out mobile inspection has been systematically developed. Field tests in multiple tunnels prove the capability of clearly achieving the acquisition of tunnel lining images. Aided by related algorithms for automatic identification and segmentation of cracks and extraction of crack geometric parameters [18; 22; 23], which have been extensively studied, the mechanized and intelligent operation for the whole-process tunnel crack collection, analysis, and statistics can be realized. In order to improve the efficiency of tunnel motion detection, the method of fast acquisition parameter calculation is established. The overall parameter calculation time is much less than one second by using the proposed calculation method. The motorized setting of a single lens parameter takes less than 10 s, the aperture adjustment takes 10 s, and 19 cameras take a total of 381 s. When the parameters are completely adjusted manually without the proposed method, it takes nearly 80 s to test and set the parameters of a single camera. Taking a two-lane tunnel as an example, it only takes 90 s for a tunnel inspection vehicle to detect at a speed of 40 km/h. As shown in Figure 15, the total detection time of the proposed method and without the proposed method is 471 s and 1610 s, respectively, which can be completed by only one driver and one operator. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Width Stage** & **Measured Width/mm** & **Gray Difference** & **Number of Gray** & **Calculate** & **Error Rate** \\  & & & **Pixels** & **Width/mm** & \\ \hline No.1 linear interval & 0.50 & 31 & — & 0.546 & 9.2\% \\ No.2 linear interval & 2.05 & 63 & — & 1.875 & 8.5\% \\ Flat interval & 3.25 & — & 7 & 3.1–3.5 & 7.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Crack width calculation comparison table. Figure 14: Crack width calculation picture, (**a**) the crack in No.1 linear interval, (**b**) the crack in No.2 linear interval, (**c**) the crack in the flat interval. If the traditional pure artificial apparent disease detection method is adopted, it is necessary to interrupt the traffic and close the road to search for diseases such as cracks in the tunnel. For the cracks located in the upper part of the tunnel, an additional climbing car is needed to complete the collection of the geometric parameters of the crack disease. This manual detection method is inefficient, and there are also potential safety hazards in the operation of climbing vehicles. For a one-kilometer-long tunnel, the manual inspection method usually requires 4-5 experienced inspectors, and the operation takes 12-24 h. In the manual collection method, the recording of cracks is mainly based on the parameter record table and discrete crack photos. It is not possible to form a spliced image of tunnel cracks, and it is not convenient to compare the development of cracks in different periods. However, the manual detection method can analyze the causes of cracks by tapping on the cracks, drilling holes, etc. In addition, for extremely dangerous cracks, it can also be disposed of in time on the spot to prevent secondary disasters from occurring, which is the ability that the tunnel inspection vehicle detection method does not have. The proposed system is compared with MIMM-R, MTI-100, ODVS, and aircraft tunnel detection equipment. As shown in Table 5, the proposed system and MIMM-R have significant advantages over other systems in moving speed due to the use of automobile chassis. Since it is mainly used in road tunnels with larger cross-sectional dimensions, the number of cameras in the proposed system is the largest. In addition, the large number of cameras and the high resolution of a single camera enable the crack identification accuracy to reach 0.1 mm, which has the best realization among devices with existing data. However, this system requires an automobile as a chassis and 19 high-resolution cameras, so it has a disadvantage compared to other systems in terms of system development cost.