## 2 Materials and Methods
### Background
#### 2.1.2 New Technologies for Inspecting Pavement Deterioration  
Among the technologies available for detection of deterioration in pavement, the laser scanner is one of the most recent [28]. It is based on two principles: flight time and phase change. Flight time sensors estimate the distance between the target and the instrument center by measuring the elapsedtime between the emitted and reflected signal, while phase shift sensors are based on the measurement of angular displacement between the emitted and reflected signal. The output obtained from this technology is a dense point cloud. The point cloud, in conjunction with algorithms, can be used to automatically detect road potholes [29]. In addition, the point cloud can be used for general road asset management due to the large amount of information it contains [30, 31]. Lasers have been proven to provide high accuracy but it is an expensive resource [32]. In response to the above, coupled with recent advances in both high-resolution digital cameras and image processing techniques, it is now possible to derive three-dimensional models from images obtained, for example, from a UAV, with sufficient accuracy and high efficiency [33, 34]. Therefore, image-based three-dimensional models can be an economical substitute for three-dimensional laser scanners [35]. A UAV is an aerial vehicle that can fly without a human pilot [36]. UAVs have become extremely popular, especially in civil applications, due to their low cost and practicality [36, 37, 38, 39]. A growing area of interest for UAV applications is the periodic inspection and evaluation of infrastructures, which is currently an expensive and time-consuming task [40]. Research has already been carried out that evaluates the UAV as a technology for data capture in pavement inspection applications, with good results due to many advantages it has [10, 16]. The operating cost for road network inspection is potentially lower than that of using monitoring vehicles. UAVs can approach scenes that would otherwise be difficult to access or even unattainable under safe circumstances [41]. However, at a certain flight height, the spatial resolution of these images limits the ability to detect certain pavement deteriorations, such as individual cracks, because their widths may be less than 1 cm. In addition, especially due to legal limits and battery life, this technology is not usually an alternative as the length of the road to map increases [35]. For 3D reconstruction from image processing, one of the techniques currently used is Structure from Motion (SfM). For reconstruction, SfM relies on images taken from many points of view [42]. Commonly, processor reconstruction is referred to as SfM. However, it is only a step in the flow of image processing (process for 3D reconstruction). Precisely, this step begins after a key point filtration, where the Scale Invariant Feature Transform (SIFT) algorithm and its variations are the most common approaches [43]. Typically, the SfM process ends with two outputs, a scattered point cloud, and camera parameters and image parameters. A scattered point cloud is an intermediate step in the production of much denser point clouds. These dense point clouds are produced by a process called Multi-View Stereo (MVS) from known intrinsic and extrinsic camera parameters. Comparatively, in a dense point cloud versus a scatter, the points are at least two orders of magnitude denser than the sparse point cloud [44]. Data processing begins with feature detection, by identifying common points in several different photos. Because of the fact that there are geometric distortions, it is necessary to find feature points, which are invariant to changes in scale and orientation in order to make matches across a wider region [45]. A filtration process is then applied to the characteristic or key points to ensure that only correct matches remain [43].