Pavement Crack Detection Approaches

The median-filter algorithms also have some limitations. Hwang and Haddad et al. (1995) indicated that the median filter performs quite well, but it falters when the probability of impulse noise occurrence becomes high. In their study, the median-filter algorithm was used in the second selection phase to remove influence points and patterns. Because of the method used in the first selection, the R value could be adjusted in order to reduce the influence points and patterns. As such, the median-filter algorithm could greatly reduce the influence points and patterns in their study.

Edge Detection

Liang and Sun et al. (2010) indicated that edge detection is an alternative method for identifying and classifying pavement cracks for automated pavement management systems. Wang et al. (2012) indicated that two types of edges are usually contained in natural photographs: step edges and line edges. Wang et al. (2012) also illustrated that step edges emphasize region boundaries, whereas line edges are located within the narrow regions. Figure 9 shows both step edges and line edges. In their study, step edges and line edges were there. In order to detect these two types of edges, a Sobel Edge detector and Canny Edge detector were widely used in the edge detection.

Regarding the Sobel edge detector, Abdel-Qader et al. (2003) stated that it is known for its simplicity and speed, compared to other algorithms that are computationally complex, and is based on the spatial gradient algorithm. The problem of the Sobel edge detector is that it fails to define the edge when there are many influence points or patterns in the picture. Thus, before using the Sobel edge detector, the picture should be analyzed to removing the influence points or patterns in order to detect the edges in the picture. Parker et al. (2010) described the Sobel algorithm as the image data being convolved with a Sobel mask, resulting in first-order partial derivatives for the pixel in the middle of the mask. Compared to the Sobel edge detector, the Canny edge detector is more complicated and it will provide better results. Abdel-Qader et al. (2003) indicated that the algorithm of the Canny edge detector is based on three parameters, which is the standard deviation of the Gaussian mask, and flow and thigh , which are used for thresholding to determine if a pixel belongs to an edge or not. However, Sobel mask methods are used in their study. In the second selection part in this study, a 10 x 10 size block is created to scan the matrix without repeat for not only minimizing the influence of the influence points and patterns but also detecting the edges. The edge is defined as the boundary of 0 and not 0.

Image Collection

Image collection is the fundamental step for the crack detection model. The objective of this process is to collect the photogrammetry data for the crack detection model. Memon et al. (2005) stated that photogrammetry is a well-established approach and commonly used by architects and engineers to monitor highways. At the same time, Brilakis et al. (2011) also indicated that close-range photogrammetry is characterized by low equipment cost and fast on-site data acquisition at the expense of heavy user intervention to generate 3D surfaces from points. Thus, photogrammetry was found to be an effective and efficient way to collect images in this study. Additionally, this phase is an important process in the crack detection model since the quality of the image greatly influences the crack detection result. Based on the distinctive visual characteristics stated by Koch and Brilakis et al. (2011), the surface texture inside a pothole is much coarser and grainier than the surface texture of the surrounding area. It means that the color inside the crack is darker than the surrounding areas. Thus, the images used in their study met the following principles:

• The crack could be clearly identified by the naked eye.
• The images were taken in the same lighting situation.
• The shadows and object influences were reduced.
• The color inside crack was darker than the surrounding areas.

Image Segmentation

Image segmentation is the second step for the crack detection model. This step is the fundamental process for the crack detection model. The purpose of image segmentation is to transform the original image into a binary image. The binary image could be treated as a 0 and 1 matrix, with 0 representing white and 1 representing not-white. In order to achieve this objective, an RGB selecting algorithm is used in this phase. In Koch and Brilakis’s et al. (2011) have used a histogram shape-based thresholding algorithm, which is based on the triangle algorithm presented in Zack et al. (1977)’s study. The purpose of that process was to separate the darker regions from the background for each picture. Additionally, as mentioned in the literature review, Koch and Brilakis et al. (2011) indicated that in pavement surface images, color information, in particular RGB values were not essential when performing the segmentation process with regard to defect detection. However, compared to the algorithm used in the Koch and Brilakis et al. (2011), the RGB selecting algorithm makes some improvements based on the histogram shape-based thresholding algorithm.

For the RGB (Red Green Blue) selecting algorithm, R (Red) value will be treated as the primary threshold value in image segmentation. The RGB values form the fundamental character in color information for the image, within a range from 0 to 255. When the red, green and blue values are all 255, the color of the pixel is white, when the red, green and blue are 0, the color of the pixel will be black. The reason the R (Red) value is selected as the primary threshold value was that the R value was more efficient for representing the black-white strength for pixels. In the RGB selecting algorithm, the P (i, j) represents pixels in the original image, B (i, j) represents the pixels in the binary image. If the R value of the pixel is smaller than the selecting Rsel value, the pixel will be defined as black, which is 1 in the binary image. Otherwise, the pixel will be defined as white, which is 0 in the binary image. Therefore, based on the threshold Rsel the original image will be transformed into a binary image using equation 2.

This equation is similar to the equation used in the image segmentation in the research of Koch and Brilakis’s et al. (2011) . As mentioned in the literature review, Koch and Brilakis et al. (2011) indicated that the threshold T is determined as the intensity value of a histogram point PT = [T, h (T)], which has the maximum distance to a line l = [P0, Pmax] that intersects the histogram’s origin P0 and the point Pmax indicating maximum intensity. However, in the RGB selecting algorithm, the Rsel is the threshold value for the selection. The RGB selecting algorithm could save computing cost compared to the histogram shape-based thresholding algorithm.

Factors That Affect the Quality Concrete Surfaces

From the literature review, there are 46 factors that may affect the quality of concrete surfaces and building walls. Five factors were selected as the factors related to US highway and road quality. Deason et al. (1998) have selected five categories and they are as follows: (1) warranties and guarantees (2) design standards (3) bidding/contract award procedures (4) public private cooperation and (5) research and development .Potholes and cracks in the highway are significant performance indicators of the quality of the highway. Potholes and cracks can form by poor construction or they may be formed due to other reasons such as poor quality of materials and seasonal changes.

According to Yu et al. (2011), have investigated that data from over 60 km arterial highway in Jiangsu Province of asphalt pavement, transverse crack, longitudinal crack, patch and alligator cracking are typical stressors, which have significant impacts on pavement service quality. The conversion factor (k) of different distress factors is defined as an important factor for evaluation. The damage conversion factors (k) of different stressors are defined by the impact on service quality and the value reflects different impact severities. However, it is necessary to adjust k considering environmental and traffic conditions. Yu et al. (2011) have identified that the conversion factor k of the potholes ranged from 0.8 to 1. The conversion factors of different types of distress and severity were adjusted in accordance with the impact of the distress on service quality and structural integrity.