###### Abstract  
Under the dual effects of the rapid growth of tunnel mileage and operating years, the application and research of tunnel crack identification based on machine vision are increasing with the vigorous development of machine vision. However, due to the complex environment in tunnels, it is difficult to quickly obtain tunnel lining cracks via computer visions in the tunnel. Therefore, this paper presents the design of a fast acquisition system with the geometric feature analysis for tunnel lining cracks, which has been integrated into a tunnel fast inspection vehicle with a machine vision module. Through the research on the image acquisition system of the tunnel lining, the parameter selection of the crack shooting hardware system is determined, and the fast calculation method of shooting parameters is proposed. The geometric characteristic analysis of the tunnel lining crack image is employed to calculate crack width and determine the optimal gray value of crack extraction. Field tests have been conducted in the highway tunnels in Zhejiang and Yunnan provinces in China and the result indicates that the proposed approach yields much better performance in the detection efficiency, whose time of detection is only 1%, and the number of personnel required is only 40% of the traditional pure manual method. Compared with similar systems, it also has significant advantages in crack resolution and detection speed. This research provides a means of rapid acquisition of tunnel cracks and laying a foundation for the evaluation of the service performance of the tunnel. t [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS]. structural problems of tunnels are primarily presented in apparent forms, such as cracks and water leakage. Traditional tunnel safety detection methods mainly rely on manual detection, which cannot meet the requirements of detection speed, accuracy, and safety requirements. Consequently, the need for mechanized, automated, and intelligent detection methods has become more and more urgent. In recent years, the continuous development of machine vision technology has provided a means of intelligent detection on tunnel apparent diseases [2]. Scholars have carried out several kinds of research in tunnel disease using machine vision. [PERSON] et al. in South Korea developed a mobile robot system with a line scan CCD camera and a specific light source, which can scan the tunnel lining at a travel speed of 5 km/h [3,4]. [PERSON] in Japan established an outline of tunnel scanner system, proposed a focus adjustment method based on the derivative of the image histogram, and used image processing technology to eliminate image fluctuations to achieve fast acquisition of tunnel lining images [5]. [PERSON] et al. in Spain presented the \"Tunneling\" tunnel lining inspection system with six high-speed cameras and six laser sensors. It can obtain the tunnel lining image and the three-dimensional outline simultaneously. The depth direction accuracy is 0.5 mm, and the vertical and horizontal resolutions are 1 mm [6]. [PERSON] et al. from the Japanese Pacific Consulting Company proposed the MIMM-R tunnel inspection system consisting of 20 cameras, which can operate at a speed of 50-70 km/h. When shooting at 1.5 mm pixel resolution, the system can identify cracks over 0.3 mm wide [7,8]. The TS series tunnel inspection system designed by the German Spacretec company employs a 360\({}^{\circ}\) rotating scanning head to collect images of the tunnel section lining. The range of 360\({}^{\circ}\) can reach 100 million pixels, and the distance between adjacent survey lines can reach 2-5 mm [9]. The ATLAS70 system developed by the Paris Metro in France conducts a laser scanner to detect abnormalities on the tunnel surface.