## 1 Introduction
Gaining a full understanding of the demands on the bridge's expansion joints, and how well they are performing, can enable adjustments and maintenance measures to be tailored to maximize the length of the service life with a minimum of maintenance effort [4; 5; 6; 7]. In this study, to detect problems and minimize the damage that may occur in the bridge expansion joint at an early stage, an optimal inspection method was developed by converting a vast number of bridge expansion joint gap maintenance inspection methods from existing manual inspections to automated inspections. To this end, it was decided to incorporate an M/V technique to switch from the existing manual inspection method of the bridge expansion joint device to a precise high-speed inspection method. M/V refers to the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. It refers to many technologies, software and hardware products, integrated systems, actions, methods, and expertise. Definitions of the term M/V vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to image processing, where the output is another image. The information can be used for such applications as automatic inspection, robot and process guidance in industry, security monitoring, and vehicle guidance [8; 9; 10]. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods, and expertise [10; 11]. M/V is practically the only term used for these functions in industrial automation applications. It attempts to integrate existing technologies in new ways and apply them to solve real-world problems in a way that meets the requirements of industrial automation and similar application areas [10; 12]. The primary uses for M/V are imaging-based automatic inspection and sorting and robot guidance [13; 14]. The imaging device (e.g., camera) can either be separate from the main image processing unit or combined with it, in which case the combination is generally called a smart camera or smart sensor [15; 16]. While conventional (2D visible light) imaging is most commonly used, alternatives include multispectral imaging, hyperspectral imaging, imaging various infrared bands, line scan imaging, 3D imaging of surfaces, and X-ray imaging [13]. We used the method of line scan imaging, which we think is most effective in technology that can automatically acquire images while driving at high speeds. [17; 18; 19; 20]. One of the purposes of the research is to use a line-scan camera in M/V to record road surfaces as images with a resolution of 1.0 mm per pixel in a high-speed (100 km/h) environment. Existing line-scanning cameras are used for the purpose of identifying counterfeit bills and inspecting semiconductor wafers' surfaces on belt conveyors, so they can be said to be the best technology for introducing to fast-moving vehicles on roads [21; 22]. This is suitable for this study because it is free from the problem of lens distortion that may occur in an area-scan camera [23]. According to this study, we developed a survey system equipped with line-scan cameras, Global Positioning System (GPS), and a distance measurement instrument sensor (DMI) in the vehicle, and as a result, we could obtain a safe and accurate image without blocking the road [24; 25; 26; 27; 28]. Recently, deep-learning-based approaches [29] have been applied to many problems in various industrial and academic fields. Visual recognition tasks, which extract information of interest from images, such as image classification [30], object detection [31], and semantic segmentation [32], have been actively studied. In particular, CNN (convolutional neural networks) have shown successful results in many visual recognition applications.