## 1 Introduction
### Literature Review  
In past decades, multiple research and development projects have addressed the problems arising from road pavement distress. This includes research on pavement distress prediction [3], association between pavement distress and risk of road accidents [4], and pavement distress prevention [5; 6]. In addition, considerable research efforts have been focusing on pavement distress detection. Pavement distress detection research can be categorized based on input data and methods of collecting input data (see Table 1). While images remain the most widely used input data type, (ground penetrating) radar and 3D data (laser or LiDAR scanning and stereo-imaging) are also quite commonly used, whereas acoustic and other types of input data are employed rarely. Figure 1: Left: Vehicles carrying the MMS. Right: Ladybug 5+ Imaging System. In order to obtain better detection performance, many systems combine several approaches for data acquisition and measurement. For example, LiDAR technology allows acquiring a subsurface profile with elevation information in addition to discovering changes in the properties of material [49], while laser based systems provide the possibility of performing automatic analysis of surface characteristics such as evenness and skid resistance. Unfortunately, these otherwise excellent solutions have one important drawback: most of such systems operate at relatively low speed, e.g., under 10 km/h. Not only does this increase the time and cost of data acquisition, operation at such a low speed in daily traffic will also decrease road traffic safety [50]. Good examples of such complex systems are ARAN 9000 developed by University of Catania and a mobile mapping system S.T.I.E.R. [12; 51]. Both systems consist of several laser based measurement devices for texture analysis and range finding, as well as of several high speed cameras. It is worth mentioning that in all works, cameras are placed orthogonally downward, facing the road pavement [52; 53; 12; 51]. Moreover, in most cases, surface cameras are synchronized with a high performance lighting unit that makes the system independent of exterior lighting conditions and shadows cast by various roadside objects and allows working with different types of pavement, from concrete to dark asphalt, one lane at a time. Additionally, pavement distress detection research can be categorized based on which defects are detected. While most research is aimed at detecting cracks (with or without other defects), some approaches, such as [40; 41; 54], focus solely on detecting potholes. In this paper, we focus on image based crack detection methods (see Section 1.1 for a discussion on input data). Pavement distress detection research on image based input data has applied a variety of methods to enhance input data and to detect or classify defects. Image-based pavement crack detection methods fall into the following main categories (see also Table 2): intensity thresholding, edge detection, graph theory, texture analysis, machine learning algorithms (e.g., support vector regression), and (deep) neural network based methods. Thresholding algorithms are based on the assumption that cracks are represented by local intensity minima; thus, binarization of the images will distinguish image areas with cracks from non-crack areas. The very well known Otsu thresholding method was widely used for pavement crack detection [55; 56]. In order to avoid illumination variations and shadows, the thresholding of the localized area has been applied [57; 58]. In [59], automation of the threshold selection was proposed. For more complex cases, advanced image analysis such as Gabor filters [23] have been used. Edge detection techniques include the usage of Canny filters, the Sobel edge detector, and other morphological filters [60; 61; 62]. With the development of artificial intelligence methods, new automated techniques for pavement distress detection have been designed.