## 1 Introduction  
The increased distress on roadway pavements owing to the heavy loads from vehicles adversely affects the durability of pavement structures, the drivability of road surfaces, and overall road conditions [1, 2]. Pavement management systems (PMS) are employed by highway administrations to maintain good driving conditions, and for efficiently manage constant road maintenance and repair works. As a necessary input of the PMS, present pavement condition is included, which provides an evaluation of the road network [3, 4]. There are several characteristics that can be assessed in pavements, but are usually classified in surface characteristics (including longitudinal profile, roughness, and surface texture and skid resistance), pavement distresses, structural evaluation, and sub-surface characteristics. However, there is not a universal approach and each highway administration collects pavement condition data following its own criteria [5, 6]. Moreover, there are various indices for measuring the same characteristic [7, 8]. Several methods have been established for collecting above mentioned information. For example, the falling weight deflectometer (FWD) test is used to assess pavement-bearing capacity [9]; however, since the FWD test needs to be carried out during a road closure, it can only be done in detailed surveys. Alternative assessment methods use the pavement serviceability index (PSI) [10, 11] and the maintenance control index (MCI) [12, 13]. The PSI is used to identify visible distress on pavement, such as cracking, patching, slope variance, and rut depth. In Japan, the MCI is used as an index of road pavement conditions instead of the PSI, which is defined by crack ratio, rut depth, and smoothness. However, in order to obtain the PSI and MCI comprehensively, it is necessary to measure the unevenness of the road surface using laser technology or other means, which makes the inspection costs high. In addition, due to the difficulty of procuring the inspection system, such inspections are rarely conducted except for critical roads such as highways. However, even on public roads, it is necessary to avoid the worsening of driving performance and safety due to pavement deterioration, so it is necessary to conduct inspections and make necessary repairs. Therefore, in the inspection of pavements, it is common to evaluate the crack ratio, which is relatively easy to measure. Cracking is one of the most common asphalt pavement road failures and an important indicator of structural deficiencies [14]. The most common crack evaluation method is visual evaluation by engineers during a patrol. However, this method has the disadvantage of being labor-intensive and inconsistent because it involves subjective judgments [15; 16]. In addition, the visual inspection requires an enormous amount of recording time and is prone to error. Therefore, there is a need for innovation in road inspection through the development of efficient and quantitative crack evaluation methods. In recent years, some studies have been carried out on ways to automatically detect cracks using a digital camera mounted on a vehicle and image processing from the images taken. This allows the inspection to be carried out simply while driving the vehicle, which significantly reduces the amount of labor required during the inspection. In addition, because the analysis is performed automatically from the images taken, there are no individual differences among the inspectors, so consistency can be maintained. For example, the Single Shot Multibox Detector (SSD) was used by [PERSON] et al. [17] and You Only Look Once v2 (YOLOv2) was used by [PERSON] et al. [18] to detect the location of cracks. However, these object detection methods can only be used to specify the cracks in the image as rectangles, and it is difficult to evaluate the degree of cracking. On the other hand, a method to detect cracks at pixel level is also considered. For example, [PERSON] and [PERSON] [19] performed semantic segmentation using U-Net, and [PERSON] et al.