###### Abstract  
Considering the limitations of the current pavement crack damage detection methods, this study proposes a method based on digital image processing technology for detecting highway asphalt pavement crack damage. Firstly, a non-subsampled contourlet transform is used to enhance the image of highway asphalt pavement. Secondly, the non-crack regions in the image are screened, and the crack extraction is completed by obtaining and enhancing the crack intensity map. Finally, the features of cracks are extracted and input into the support vector machine for classification and recognition to complete the detection of cracks in highway asphalt pavement. The experimental results show that the proposed method can effectively enhance the quality of a pavement image and precisely extract a crack area from the image with a high level of damage detection accuracy.  
non subsampled contourlet transform; pavement cracks; feature extraction; support vector machine; damage detection +
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operator could effectively amplify the difference between the crack and non-crack areas, and the corresponding crack mapping image could be obtained through the processing of this operator. Finally, dynamic threshold segmentation and denoising are performed on the mapped image to obtain the final binary image of cracks and achieve crack damage detection. However, this method does not screen for non-crack areas in the image and cannot accurately extract crack areas, which affects the effectiveness of damage detection.  
[PERSON] et al. proposed a crack detection method for asphalt pavement based on neighborhood and gradient saliency feature fusion [6]. The image was denoised using grayscale correction and morphology. This process involves extracting important features in the image's vicinity based on the significant differences between pixels and their surrounding arias. Significant features in different directions are obtained through directional adjustable filters, and feature fusion salient maps are generated through convolutional operations. Then, the feature fusion salient map threshold is segmented to obtain suspected crack aggregation areas, introducing a clustering analysis method to select crack candidate regions based on the different geometric features of the clustering region. Finally, a region endpoint search and localization method is proposed, which eliminates subsets of non-endpoint aggregation regions and connects endpoints of different regions, ultimately achieving complete crack extraction. However, this method does not enhance the road surface image, resulting in certain errors in image recognition, inconsistent damage detection results with actual results, and reducing the accuracy of damage detection.  
In order to solve the issues in the above methods, a method based on digital image processing technology for detecting cracks in expressway asphalt pavement is proposed. The main steps are as follows:  
1. Use the nonsubsampled contourlet transform to enhance the image of asphalt pavement on highways, denoise, and select thresholds through principal component analysis;
2. Use the Gaussian Laplace operator to filter non-crack images and obtain intensity images based on the non-negative features of cracks;
3. Enhance regional information and extract crack features through comparative crack features;
4. Use projection features and crack pixel features to classify crack images. The features serve as input vectors for support vector machines, and when combined with classification functions, the detection of crack diseases in highway asphalt pavement is completed;
5. Experiments and discussions show the overall effectiveness of the proposed method through image enhancement, crack extraction, and damage detection tests;
6. Conclusions.