## 4 Experiments
### Highway Crack Classification Experiments
#### 4.2.3 Highway Crack Classification  
After a series of data preprocessing operations, the CrackClassification algorithm can be used to classify the cracks in the crack detection result image shown in Figure 18c. By comparing with the classification results of manual inspection during the same period, the confusion matrix of crack classification accuracy can be obtained, as shown in Table 9.  
For real transverse cracks, 86.8% were detected as transverse cracks, 6.2% were detected as longitudinal cracks, 3.6% were detected as block cracks, and 3.4% were detected as reticulated cracks. For real longitudinal cracks, 7.9% were detected as transverse cracks, 84.9% were detected as longitudinal cracks, 4.8% were detected as block cracks, and 2.4% were detected as reticulated cracks. For real block cracks, 5.9% were detected as transverse cracks, 4.1% were detected as longitudinal cracks, 73.8% were detected as block cracks, and 16.2% were detected as reticulated cracks. For real reticulated cracks, 2.9% were detected as transverse cracks, 2.7% were detected as longitudinal cracks, 17.5% were detected as block cracks, and 77.2% were detected as reticulated cracks.  
The evaluation metrics used for highway crack classification are precision and recall. The formulas for precision and recall of each crack category are shown below:  
\[Precision_{i}=\frac{M_{i,i}}{\sum_{j=1}^{4}M_{j,i}} \tag{10}\]  
\[Recall_{i}=\frac{M_{i,i}}{\sum_{j=1}^{4}M_{i,j}} \tag{11}\]  
Figure 17: Highway roadbed pixel coordinates txt file.  
where \(Precision_{i}\) represents the classification accuracy for type \(i\) cracks. \(M_{i,i}\) represents the number of samples where the true class is type \(i\) crack and the predicted class is type \(i\) crack. \(M_{j,i}\) represents the number of samples where the true class is type \(j\) crack and the predicted class is type \(i\) crack. \(M_{i,j}\) represents the number of samples where the true class is type \(i\) crack and the predicted class is type \(j\) crack.  
According to the confusion matrix, the precision and recall for each type of crack can be calculated, as shown in Table 10. The precision for horizontal, vertical, block, and reticulated cracks are 0.839, 0.867, 0.740, and 0.778, respectively. The recall for horizontal, vertical, block, and reticulated cracks are 0.868, 0.849, 0.738, and 0.772, respectively.  
In this experiment, crack detection and classification were conducted on 108 km of three highways, and relevant information such as crack type and location were written into a database. By comparing our crack classification data with the corresponding manual road inspection data from that year, we found that the classification accuracy for transverse and longitudinal cracks reached about 85%, while the classification accuracy for block and reticulated cracks reached about 78%. These results confirm the scientific and rational nature of the classification scheme and algorithm used in this study.  
Figure 18: The environmental noise removal experiment with images showing (**a**) the full-size drone aerial image, (**b**) the drone aerial image with environmental noise removed, and (**c**) the crack detection result image for the road surface part. The two arrows from left to right represent the process of cropping the environmental parts from the full-size aerial image based on the roadbed pixel coordinates and detecting cracks on the road surface part of the aerial image.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Predict Results**} \\ \cline{2-5}
**Real Results** & **Transverse** & **Longitudinal** & **Black Cracks** & **Reticulated** \\  & **Cracks** & **Cracks** & **Cracks** & **Cracks** \\ \hline Transverse Cracks & 86.8\% & 6.2\% & 3.6\% & 3.4\% \\ Longitudinal Cracks & 7.9\% & 84.9\% & 4.8\% & 2.4\% \\ Block Cracks & 5.9\% & 4.1\% & 73.8\% & 16.2\% \\ Reticulated Cracks & 2.9\% & 2.7\% & 17.5\% & 77.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The confusion matrix for the accuracy of the crack classification.