## 1 Introduction  
Cracks in concrete structures is some of the most important indications (or predictors) for significant structural distress or damage caused by various causes such as aging. If the crack is reliably inspected, severe damage (or possible failure) to concrete facilities can be effectively forestalled, and the life of the facilities can be lengthened through appropriate maintenance. Conventional crack inspections are performed by trained inspectors who visually detect cracks and then measure their length and width using suitable measuring tools (crack rulers, crack microscopes, etc.). Although human-based inspection may be effective, the objectivity and reliability of the records for the cracks may easily be reduced because inspection relies on the subjectivity of the inspector. In addition, it is difficult to determine the progress of damage if the inspector changes. Thus, these drawbacks have led to extensive research on crack inspection that secures objectivity and accuracy while enabling the convenience of recording and storing data. Over the past few decades, image processing (IP)-based crack inspection techniques have been widely studied. This processing refers to the entire computational process of the photograph of a concrete structure; image input/output, digitization, segmentation, defect management, and defect detection. Thus, it not only detects cracks but also measures the width and orientation of the recognized cracks. IP-based methods generally determine whether or not an image pixel is part of a crack region by using predefined gradient filters and a binary classifier. [PERSON] and [PERSON] showed that it is possible to detect crack by distinguishing darker areas [1,2]. Many researchers proposed effective crack detection that enhances cracks using noise filtering on the images [3,4]. Moreover, various image processing techniques for crack inspection have also been studied based on morphology [5, 6, 7], fuzzy [8,9], and percolation [10,11] techniques. However, the accuracy of these rule-based image processing is variant to the focal length of the image, the influence of the shooting environment (illuminance, intensity, etc. ), shooting quality, and the resolution. This weakness requires new filter modeling every time depending on the input image for accurate detection. To improve the rule-based crack inspection methods, machine learning (ML) has attracted attention from researchers. Machine learning refers to computer algorithms learning important patterns or rules by itself based on given data without being explicitly programmed. ML-based crack inspection methods are categorized into two main groups as artificial neural networks (ANN) use or without ANN use. Conventional ML-based crack inspection methods [12, 13, 14, 15, 16] first extract crack features using image processing, then evaluate whether or not the extracted features indicate cracks without ANN use. Many researchers have used various ML algorithms for crack detection, including Support Vector Machine [12,13], Bayesian [14], genetic algorithm [15], and decision trees [16]. However, conventional ML-based methods have limitations as their accuracy inevitably relies on the extracted crack features by image processing. Recently, as another category, ANN-based crack detection techniques are actively being researched. As one of the machine learning techniques, ANN learns given data inspired by biological neural networks. Particularly, deep ANN (i.e., deep learning) [17] is acclaimed with outstanding performance. Previous studies have shown that the ANN-based method can contribute to overcoming the difficult or infeasible problems of the IP-based methods. For example, [PERSON] et al. [18] showed that the ANN using a restricted Boltzmann machine can successfully detect a crack in spite of the limited training samples. [PERSON] et al. [19] demonstrated that the crack in a road image can be determined with an accuracy of 87% using the convolutional neural network (CNN) consisting of only four layers. More recently, [PERSON] et al.