## 4 Experiments
### Highway Crack Classification Experiments
#### 4.2.2 Environmental Noise Removal Based on Roadbed Identification  
In order to avoid classifying the detected environmental noise as cracks in the classification process, we can first identify the roadbed and obtain its pixel coordinates. Based on the obtained pixel coordinates of the roadbed, the environmental noise in the full-size UAV aerial image can be removed, leaving only the highway pavement portion. Then, the trained CrackNet network can be used to detect cracks in the UAV aerial image of the highway pavement, resulting in a crack detection result map for the pavement portion. Finally, the CrackClassification algorithm can be applied to the crack detection result map of the pavement to achieve crack classification. In this way, the impact of ambient noise can be greatly reduced, and GPU resources can be greatly spared.  
Due to the lack of a public dataset for UAV remote sensing roadbeds, a dataset, as is shown in Figure 15, was created by hand labeling, which is the second dataset created in this study. The creation process is similar to that of UAV remote sensing road crack dataset. The structure of the roadbed dataset is shown in Table 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Original Images** & **Ground Truth** \\ \hline Training set & 7025 & 7025 \\ Validation set & 2356 & 2356 \\ Test set & 2356 & 2356 \\ Total & 11,737 & 11,737 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Highway roadbed dataset.  
Figure 14: The classification effect of YOLOv5 on a UAV remote sensing highway crack detection map.  
Since the roadbed also belongs to the edge information and is less difficult to identify compared to the road cracks, the DenxiDeepCrack model [35] is trained to identify the roadbed. The results of the roadbed inspection are shown in Figure 16a.  
Since the yellow wire in the center of the highway may be identified as the roadbed in the detection results, it must be removed to more accurately obtain the coordinates of the roadbed. The central yellow line removal algorithm can be described as follows: First, binarize the original roadbed inspection result map. Then, subtract the central yellow line detection result matrix from the original roadbed detection result matrix after binarization. Since the yellow line is very thick, as shown in Figure 16b, there will be negative values in the roadbed detection map matrix after obtaining the difference, and the image matrix is numerically processed with negative values set to 0. The image matrix after removing the central yellow line is rewritten, and the roadbed detection results after removing the central yellow line are shown in Figure 16c.  
Figure 16: The yellow line removal experiments with diagrams showing (**a**) the roadbed inspection map with the central yellow line, (**b**) the central yellow line inspection map, and (**c**) the roadbed inspection map after processing with the yellow line removal algorithm, where the central yellow line has been removed.  
Figure 15: A part of the UAV highway roadbed dataset.  
Then, the roadbed pixel coordinates are obtained and recorded in the roadbed inspection result map with the central yellow line removed. A text file with the same name as the inspection result map is created to record the pixel coordinates of the roadbed. The generated roadbed pixel coordinates txt file is shown in Figure 17.  
After obtaining the pixel coordinates of the roadbed, the drone aerial image, as shown in Figure 18a, is cropped based on the roadbed pixel coordinates, removing the environmental part and retaining only the road surface, as shown in Figure 18b. Then, the crack detection network trained in Section 4.1 is used to detect cracks on the cropped image without the environmental noise, as shown in Figure 18c. This approach not only removes the environmental noise but also saves substantial GPU resources. Compared to directly detecting cracks on full-size images and then removing environmental noise based on roadbed recognition, this approach saves about 65% of GPU workload.