# An Improved Crack Identification Method for Asphalt Concrete Pavement
## 2 Methodology
### Classification and Identification Method of Cracks  
#### 2.3.1 Crack Direction Identification  
Based on the segmented asphalt concrete pavement crack image, the crack direction in the image is recognized. The cracks of highway concrete pavement can be divided into linear cracks and net-shaped cracks, and the former can be subdivided into transverse, longitudinal and oblique cracks [19; 20; 21]. Because the disease parameters to be extracted from net-shaped and linear cracks are different, the cracks must be classified first, using the classification algorithm. This paper classifies the cracks in two steps. First, the method of marking the background connected region curve is used to distinguish the linear cracks from the net-shaped cracks, and then the image projection method is used to classify the linear cracks.  
The method of marking connected domain is used to distinguish linear and net-shaped cracks. The image background area is divided into more than three connected domains by cracks, while the background of linear cracks is generally divided into two connected domains. Finding the number of background connected components from the labeling algorithm \(N\), compare it with the threshold value \(T_{n}\). If \(N\) is greater than \(T_{n}\), it is a net-shaped crack; if the reverse, it is a linear crack.  
If the pixel size of the crack image is \(M\times N\), the pixel value of line \(i\), column \(j\) is \(I(i,j)\) and projections on \(X\) direction and \(Y\) direction are \(X(j)\) and \(Y(j)\), there are:  
\[\left\{\begin{array}{l}X(j)=\sum\limits_{i=1}^{M}I(i,j),j=1,2,\cdot\cdot \cdot,N\\ Y(i)=\sum\limits_{i=1}^{N}I(i,j),i=1,2,\cdot\cdot,M\end{array}\right. \tag{19}\]In order to automatically identify the linear crack category, the difference operation is performed on the horizontal and vertical projection sequences, and the maximum difference value is calculated:  
\[Q_{r}=\max\left|X_{r}(j+1)\right|-\left|X_{r}(j)\right| \tag{20}\]  
\[Q_{c}=\max\left|Y_{c}(i+1)\right|-\left|Y_{c}(i)\right| \tag{21}\]  
\[T=\left|Q_{r}\right|-\left|Q_{c}\right| \tag{22}\]  
Among them, \(Q_{r}\) is the maximum difference value on the horizontal projection, \(Q_{c}\) is the maximum difference value on the vertical projection, \(X_{r}(j)\) and \(X_{r}(j+1)\) are values adjacent to \(X(j)\), and \(Y_{c}(i)\) and \(Y_{c}(i+1)\) are values adjacent to \(Y(i)\).  
For transverse cracks, \(Q_{c}\) is much greater than \(Q_{r}\); for longitudinal cracks, \(Q_{r}\) is much greater than \(Q_{c}\); and for diagonal cracks, the values of \(Q_{c}\) and \(Q_{r}\) are equivalent. Set a threshold value for transverse and longitudinal cracks \(T_{0}\); when \(T\) is greater than \(T_{0}\), the crack is horizontal and vertical; otherwise, it is ab inclined crack. Finally, compare the values of \(Q_{c}\) and \(Q_{r}\); this can be used to determine whether the crack is transverse or longitudinal.  
After the above steps, the identification of the distribution types of cracks in asphalt concrete pavement is realized. However, it is far from enough to only identify the types of cracks in asphalt concrete pavement. It is also necessary to identify the width, area, etc., of the cracks, in order to provide detailed information for the maintenance of asphalt concrete pavement.