# Research on Feature Identification and Trajectory Planning of Pavement Cracks
## 3 Intelligent pavement Crack Detection and Repair Algorithm
### Image Filtering Stage
#### 3.2.2 Wide Crack Feature Extraction Policy  
In road repair work, high-density noise wide crack type images are a common type of image captured by system cameras. In this type of image, the main crack is often thick, and dense noise points are commonly distributed at both ends, which brings a certain degree  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**High Density Noise Wide Crack Type**} & \multicolumn{4}{c}{**Low Density Noise Narrow Crack Type**} \\ \hline
**Sample** & Sample I & Sample II & Sample III & Sample IV & Sample I & Sample II & Sample III & Sample IV \\ \hline
**Global Variance Value** & 898.5224 & 947.9831 & 806.3760 & 1124.1458 & 129.5714 & 116.9045 & 287.2469 & 221.6327 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global Variance Calculation Results.  
Figure 3: Comparison of the original binarization effect. (**a**) indicates the effect for wide crack type, (**b**) indicates which for narrow crack type.  
of difficulty to the extraction of main crack features. In general, there are the following feature extraction methods for crack scene images: 1. Otsu thresholding method [5]; 2. Image enhancement combined with segmentation methods [7]; 3. Canny operator edge detection [8]; 4. grayscale morphological filter [14]; 5. histogram equalization [15]; 6. median filter [16]. However, the above traditional filtering methods are all based on pixel neighborhood feature values to filter images indiscriminately. Applying it directly to filter this type of image is prone to the situation that the noise group and the feature cannot be separated or the crack feature is damaged during filtering. Therefore, in order to filter the dense noise point groups and retain the main crack feature, the algorithm in this paper combines the median filter and the Otsu thresholding method, and proposes a feature extraction policy for high-density noise wide crack type images.  
High density noise wide crack type images have Tamura features with large roughness, small contrast and scattered directions. Compared with its opposite category, the cooccurrence variance of its Gray-level Co-occurrence Matrix is larger, so it is not suitable to directly use traditional morphological segmentation method for filtering [17]. The algorithm in this paper firstly uses the median filter method to smooth the original image texture, neutralizes the image roughness, and greatly reduces the possibility of noise clustering. The median filter replaces each pixel in the image with the median value of the sequence of its eight neighborhoods, so that the surrounding pixels are close to the real value, thereby eliminating isolated noise point. As shown in Figure 4, the algorithm uses a \(3\times 3\) two-dimensional rectangular sliding window to sort the pixel values in the window to generate a monotonically increasing data sequence. The filtering effect is shown in Figure 5. The median filter can smooth the texture of the original crack image on the basis of retaining the main crack feature to the maximum extent.  
After median filtering, the image has already have good contrast and regularity. At this time, the traditional morphological segmentation method can be used to accurately extract the main crack feature. The algorithm uses the Otsu thresholding method to binarize the image. By setting the threshold to maximize the variance between the crack and the background class, a binarized image that only contains crack features is obtained [18]. As  
Figure 4: Median filtering sliding window.  
Figure 5: Comparison of median filtering.  
shown in Figure 6, the Otsu thresholding method accurately removes all the noise groups in this type of crack image, and retains the main crack feature relatively completely. This paper verifies the reliability of the model by comparing the actual effects of various feature extraction and combination methods, as shown in Figure 7.