## 7 Conclusions  
This paper firstly develops the hardware system of the tunnel inspection vehicle and studies the hardware parameter selection and parameter calculation method of the image fast acquisition system. Then, images characteristics of cracks in the tunnel lining are analyzed, and practical tests are carried out at the tunnel site. Finally, a comparison is also made with pure detection. The main conclusions are as follows:  
The hardware design and integration of the image acquisition system is carried out, and a tunnel inspection vehicle is systematically developed. The selection of the hardware parameters of the image capture system is determined, and the method of fast acquisition parameter calculation is formed so that the three parameters of lens zoom, camera focus, and camera attitude can be calculated before entering the tunnel.  
In the analysis of lining crack images, the model of tunnel lining crack width is established. The extraction of crack width is divided into three intervals according to the grayscale difference. In the No.1 linear interval and the No.2 linear interval, a linear fitting relationship between the grayscale difference and the crack width is established. The calculation relationship between the number of pixels and the crack width is established in the flat interval. In addition, in order to facilitate crack identification, it is analyzed that when the gray value of the background is 140, it has the highest gray contrast of crack pixels.  
Field tests are carried out in many operated tunnels in Zhejiang and Yunnan provinces. The developed hardware system and software parameter calculation method can clearly obtain images of tunnel lining cracks. In the three crack width stage, the maximum width error rate of the crack extraction model is 9.2% in the No.1 linear interval. The time of detection method using the tunnel detection vehicle and automatic parameter calculation proposed in this paper is only 30% of the manual parameter adjustment and 1% of the traditional pure manual detection, and the amount of labor required is only 40% of the traditional method. In addition, there are also significant advantages in terms of automation. Furthermore, compared with other recent systems in terms of hardware type, quantity, resolution, and movement speed, it shows that this system has significant advantages in crack identification accuracy and movement speed.  
Although we have realized some achievements in the rapid acquisition of tunnel lining images and the analysis of crack image characteristics, there is still much work to explore. For example, further improvement of crack width extraction accuracy, automatic adjustment of apertures for different lining materials, and assessment of the safety status of tunnel structures using the detected tunnel crack conditions are all important issues that need to be solved urgently to ensure the safe operation of tunnels.  
Conceptualization, Q.W., X.X. and B.Z.; methodology, H.W.; software, J.C.; validation, J.Z. and H.W.; resources, Y.L.; data curation, D.Y. and W.Z.; writing--original draft preparation, H.W.; writing--review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation Committee Program of China (Grant No. 52038008, 42107216), the Key Science and Technology Projects in Transportation Industry by Ministry of Transport of the People's Republic of China in 2021 (Grant No. 2021-MS2-061), the Project of Science and Technology Program of Department of Transport (Grant No. 2021014), the Independent Scientific Research Project of Zhejiang Scientific Research Institute of Transport ( Grant No. ZK202205, Grant No. ZK202206).  
**Acknowledgments:** Thanks to the Bureau of Transportation of Lishui and Yunnan Wuyi Expressway Constructing Headquarters for providing the tunnels for detection.  
**Conflicts of Interest:** The authors declare no conflict of interest.