## 1 Introduction  
Civil infrastructure is regularly inspected to prevent accidents after structural damage. However, the structures requiring maintenance outnumber the available maintenance personnel, leading to difficulties in the systematic maintenance of these facilities [1; 2; 3]. Bridges are an integral part of the transportation system as they improve road connectivity over inaccessible terrain. These structures carry heavy vehicular loads throughout their service life, which may result in premature or end-of-life structural failure and a safety risk to the public. Therefore, regular and meticulous monitoring of bridges is imperative. In most cases, including special events such as extreme weather and disasters, personnel routinely inspect bridges to detect and identify damage [4]. This type of manual inspection is expensive and requires professional input on bridge damage. Damage detection is also affected by the parameters of devices used to collect image data, such as the camera angle, and the location. These limitations hinder the achievement of a reliable level of detection and identification accuracy. To address these problems, active research using various approaches is underway. The early stages of research were aimed at detecting cracks on the surface of bridges, because most of the other types of damage to aging bridges result from these cracks. Thus, algorithms such as canny and crack-forest and machine-learning techniques such as graph convolutional network (GCN) were used to detect crack edges [2]. More recently, intelligent bridge testing systems and inspection technologies exploiting unmanned aerial vehicles and image-processing techniques have been developed [1; 4; 5; 6]. However, these are still fledgling technologies. Technologies for identifying different shapes and segmentations in captured images require further development. Currently, professional input is still required for manually identifying the damage type through visual inspection of captured images. Although studies have implemented deep learning (DL) algorithms [7; 8; 9], many of them were based on precise measurement at the laboratory scale, or their results were not commercially viable owing to the limited availability of data. Furthermore, as these studies did not use field-inspection images, the results of their model training did not accurately reflect the real-world bridge damage. Recently, studies have investigated techniques to assess facility structures using DL models [1; 2; 5; 8; 9; 10; 11]. The convolutional neural network (CNN) has reportedly exhibited excellent performance in classifying and detecting objects using training image datasets. [PERSON] et al. applied deep convolutional neural networks (DCNN) to detect six types of damage, including cracks, concrete scaling, and corrosion, in facility structures [11]. In the Republic of Korea, studies have utilized AlexNet for the detection of concrete spalling [8] and the analysis of railway bridge damage using faster regions with a convolutional neural network (Faster-RCNN) [1]. The limitations of DL-based algorithms are (1) the variation of detection performance with the training image, and (2) difficulty in classifying damage types when the classes have few features or when the features are unclear. Owing to the nature of DL technology, the greater the volume of data for training, the higher the classification accuracy. In terms of the dependence of classification performance on the clear representation of features, for the classification of damage types for bridges, into categories such as efflorescence, corrosion, cracks, concrete scaling, and spalling, the distinction between efflorescence, which appears white in color on the bridge, and cracks is highly accurate as they appear distinct, whereas the classification between scaling, spalling, and cracks is inaccurate as these are similar types of damage. A previous study proposed a preprocessing technique for training images [3]. It allowed for the use of low-resolution bridge images from collected data for training.