## 1 Introduction
In this study, models based on CNN and some deep learning techniques are applied to image analysis problems to determine the distance between expansion joints. Two CNN models are used to analyze the distance step by step. First, image classification categorizes images semantically into sub-groups. An image-classification CNN extracts important image features like texture cues and shape cues from each image, and a logistic regression [31] distinguishes them between categories based on the extracted features. Successful studies on image classification CNNs have been conducted focusing on the ImageNet benchmark dataset [33], and some design patterns have been proposed to achieve technical goals such as learning more complex patterns (ResNet) [34], light model weights (MobileNet) [35], and efficient scalability (EfficientNet) [36]. These design patterns, called CNN architectures, are applied to general image classification to reduce the need to design new models every time. Second, semantic segmentation is the process of classifying each pixel in an image belonging to a particular class. The recent success of CNN has also driven outstanding progress in semantic segmentation [37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. Based on CNN architecture, the semantic segmentation model extracts local contexts (a small area centered in a pixel to classify) and global contexts (overall semantics of input image) from an image and reconstructs contexts to create a class heatmap of the same size as the original image, which shows the probability of which class each pixel is. Therefore, it can be thought of as a classification problem for each pixel, considering local and global contexts. Another purpose of the study is to develop software that measures gap width with more than 90% accuracy in the road image of the survey so that many bridges can be monitored quickly. Therefore, an algorithm was developed to measure the gap width after first finding the expansion point in the road image using machine learning. As a result, the identification accuracy was more than 95%, and the investigation time was reduced by more than 95%, from an average of about 1 h/bridge (the inspector's existing manual inspection method) to about 3 min/bridge. The main contribution of this study is to demonstrate the possibility of developing smart maintenance techniques for road structures, in other words, the successful development of a smart maintenance system that combines machine vision and machine learning technology, for the following purposes: first, to ensure the traffic safety of vehicles on roads that are already in use; and second, to obtain the condition of road structures at the level the investigator wants with figures and accurate images. The article is organized as follows:  
* Section 2 introduces a system developed to survey the road surface while driving at high speed (100 km/h) with a line-scan camera and an M/V imaging device. It introduces operating equipment and explains the main functions and test results. * Section 3 describes the adequacy review of pre-setting by surveying newly constructed bridge construction joints with standard computer vision methods applied to the initial system. * Section 4 describes another detection mechanism that uses machine learning. * Section 5 concludes the paper and proposes future work.