# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 2 Detection Method for Skid Resistance of Asphalt Pavement  
### Highway Asphalt Pavement Skid Resistance Detection Method  
With the development of digital image processing technology and computational processing technology, scholars at home and abroad take advantage of the emerging high-precision equipment to obtain the texture data of the pavement surface and are equipped with software such as MATLAB and Python to realize the efficient processing and computation of the pavement surface texture data [22; 23]. Python and other software can realize the efficient processing and calculation of pavement texture data, of which the acquisition method of pavement surface texture currently has a close-up photography system, such as a laser scanner (the principle is shown in Figure 1); after acquiring the pavement texture data, an X-ray CT scanning system is used for data processing to complete the three-dimensional reconstruction of the pavement texture, and then the calculation of pavement texture indexes is conducted to complete the evaluation of the road surface skid resistance [24]. The basic operations of image processing technology are filtering and denoising, enhancement and restoration, image segmentation, morphological processing, etc. The basic process of image processing is as follows: two-dimensional digital images of the specimen are captured, and then the captured images of the asphalt road surface are inputted into the computer for programming and processing [26]. The digital image method does not interfere with traffic and has convenient operation and low camera cost. It helps to analyze the pavement texture characteristics but is easily influenced by the external environment. And image recognition technology is in the development stage. The accuracy is easily affected by the accuracy of the image recognition algorithm. But as the algorithm improves, the digital image method can be more conveniently applied to the detection of pavement damage in the future. It is one of the important technologies for future pavement detection [20]. The digital image method to construct the depth statistics results and the correlation between the two are shown schematically in Figure 2.  
In recent years, researchers have proposed various noise reduction methods for the digital image method [28; 29], including algorithms based on non-local means [30], sparse representation [31], wavelet transform, deep learning [32], etc. These methods are usually targeted at specific types of noise or specific scenarios, and no unified evaluation standard has been formed. Regarding the image threshold segmentation technology, existing research has shown that threshold segmentation methods can be divided into hard thresholding and soft thresholding, and these are further optimized by combining with technologies such as wavelet transform and neural networks [32]. However, due to the differences in segmentation accuracy and computational complexity among different algorithms, there is currently no unified evaluation standard to measure their performance. Regarding the evaluation of image uniformity, it mainly relies on the method of manually selecting uniform regions and calculating their mean and standard deviation [33]. Although this  
Figure 1: Road surface texture laser detection [25]. Figure 2: Schematic diagram of the statistical results of the digital image method of constructing depth and the correlation between them [27]. method is simple and intuitive, it has significant limitations. For example, it requires manual intervention and is difficult to automate. An automated close-up photogrammetry system (ACRP) is one of the important methods used to detect the skid resistance of AP. The ACPR system includes a lighting system and a lighting module. The main control computer is connected to the system, and the lighting device is controlled by the computer programmed to illuminate the lighting device. It triggers three cameras to take pictures simultaneously to complete image acquisition.