## 4 Experiment and Discussion  
In order to verify the overall effectiveness of the digital image processing technology-based crack detection method for expressway asphalt pavement, it is necessary to conduct testing. In this experiment, a Windows 10 system with 32 GB of memory was selected, equipped with an Intel Core i7-14700 KF [EMAIL_ADDRESS] GHz. The processor is equipped with an 8-bit 74 HC165D model data register, and the simulation software running is Matlab R2019a. The experimental dataset was created using a randomly collected total of 1000 road surface images, including road crack images (accounting for 75%) and crack-free images (accounting for 25%). Using the Python random sampling algorithm, the dataset is divided into a training set and a testing set, with a ratio of 8:2. The training set is trained to obtain detection results and then compare the detection results with the actual results in the test set to calculate accuracy and recall. Accuracy refers to the proportion of correctly predicted samples to the total number of samples. The calculation method is: accuracy = (true example + true counter-example)/(true example + false positive example + true counter-example + false counter-example). The recall rate refers to the proportion of the number of samples correctly predicted as positive cases (cracks) to the actual number of positive cases (true cases + false negative cases). The calculation method is: recall rate = true example/(true example + false counter-example). Among them, true positive (TP): the number of samples predicted to be cracks and actually cracked;  
True negative (TN): the number of samples predicted to be normal and actually normal;  
False positive (FP): the number of samples predicted to be cracks but actually normal;  
False negative (FN): the number of samples predicted to be normal but actually cracked. After obtaining the accuracy and recall rates, we verify the accuracy of different methods for detecting road crack images through the comparative testing F1_score index. The F1_score index is an evaluation indicator that comprehensively considers accuracy and recall. The value range of the F1_score index is between 0 and 1. A higher value approaching 1 indicates a superior performance of the application method, indicating a greater ability to detect road cracks accurately. The road crack image set used in this test is shown in Figure 4.  
It can be seen from the analysis of Figure 4 that the images used for the test include transverse cracks, longitudinal cracks, reticular cracks, and cracking. Due to the influence of acquisition equipment, lighting, and other factors, the image clarity is low. Therefore, the approach to detecting cracks in expressway asphalt pavement is based on digital image  
Figure 3: Flow chart of crack detection method for asphalt pavement on highways. processing technology, specifically the literature-based method [4]. The methods in literature [5] and literature [6] expand image enhancement processing, and the results are shown in Figure 5.  
Figure 4: Crack image set. Figure 5: Image processing results of different methods. (**a**) Proposed method. (**b**) Literature [4] method. (**c**) Literature [5] method. (**d**) Literature [6] method. Figure 5 shows that after using the method described in reference [4], strip noise ripples are added to the image, resulting in a decrease in image clarity. Upon implementing the method in reference [5], the image shows a slight distortion and serrated noise at the crack edge, which reduces the image quality. After using the method in reference [6], the image shows local exposure, an increase in highlights, and a lack of some details. After using the proposed method, the clarity of the image is enhanced, noise is reduced, and detailed information about cracks can be accurately obtained. The above tests show that the proposed method has good image effects and can enhance the detailed information in the image. Extracting the crack area of the image is the key technology of crack damage detection.