# An Improved Crack Identification Method for Asphalt Concrete Pavement
## 2 Methodology
### Image Feature Enhancement Method of Asphalt Concrete Pavement Cracks
#### 2.1.1 Pavement Crack Image Denoising Preprocessing  
In the process of image acquisition and transmission, there will be image dislocation, band noise, stripe noise and so on. In order to make the cracks in the asphalt concrete pavement crack image clearer and identify cracks more accurately, the image needs to be denoised [10; 11; 12]. Here, the NeighShrink algorithm is used to implement image preprocessing. The purpose of denoising is to eliminate the noise present in the image, making it clearer and easier to analyze and process. When collecting asphalt concrete pavement crack images, due to environmental conditions, changes in light, limitations of the image sensor itself and other factors, the image may be subject to various types of noise interference, such as Gaussian noise, Salt-and-pepper noise, etc. By applying denoising algorithms such as the NeighShrink algorithm, the noise level in the image can be effectively reduced, and the image quality can be improved. Denoising processing helps to reduce the interference of noise on image features, enabling subsequent image enhancement, segmentation and feature extraction algorithms to more accurately identify and analyze crack features. This can improve the precise identification and parameter extraction capabilities of asphalt concrete pavement cracks, providing more reliable data support for pavement maintenance and repair. The detailed process is as follows:  
Set \(Q\) as the image of asphalt concrete pavement cracks observed, and its expression equation is as follows:  
\[U=\kappa+E \tag{1}\]  
where \(E\) represents Gaussian white noise and \(\kappa\) indicates the crack information of asphalt concrete pavement without noise. Set \(j\), \(g\) to represent the scale and direction of the asphalt concrete pavement crack image, respectively; the non-downsampled contour wave transform coefficient of the image on this scale and in this direction is \(U_{j,g}\). the neighborhood of the transformation coefficient is determined by \(B_{j,g}\). Based on \(U_{j,g}\), calculate neighborhood \(B_{j,g}\), the wavelet transform coefficient of which is expressed as follows:  
\[D_{j,g}=\sum_{(i,l)\in P}U_{i,l}^{2} \tag{2}\]  
where \(D_{j,g}\) represents wavelet transform coefficients of neighborhood \(B_{j,g}\) and \(P\) represents the total number of wavelet coefficients in the image sub-band. Based on Equation (2), the image is denoised, and the expression equation is as follows:  
\[\kappa_{j,g}^{\wedge}=\frac{D_{j,g}-\theta^{2}}{D_{j,g}}+Q_{j,g} \tag{3}\]  
where \(\kappa_{j,g}^{\wedge}\) represents an image without noise, where the scale and direction are \(j\) and \(g\), respectively; \(\theta^{2}\) represents the standard deviation of the image signal; and \(Q_{j,g}\) indicates that the initial scale and direction are \(j\) and \(g\), respectively, of the asphalt concrete pavement crack image. After the above steps, the preprocessing of asphalt concrete pavement crack image denoising is realized. .1.2 Fractional Differential Image Feature Enhancement Algorithm Based on Image Feature Segmentation  
When the pixels in the asphalt concrete pavement crack image signal are adjacent in the spatial domain, they show a strong correlation, which contains a lot of structural information about the target image [13; 14]. According to this information, the image is divided into image blocks with similar structure information, then the fractional order differential order is set according to the proportion of each feature image block and, finally, the order is substituted into the mask operator for calculation. Step 1: Conduct the image structure feature segmentation method as follows. According to the structural characteristics, the original asphalt concrete pavement crack image is divided into image blocks with similar image information but different shapes. The structural features of image blocks are measured by the mean and variance of gray levels. Divide the image into \(M\times N\).