## 4 Experiments
### Highway Crack Detection Experiments
#### 4.1.4 Comparison Experiments  
The comparison experiments not only compare the CrackNet network model with other mainstream image segmentation models based on the homemade UAV remote sensing highway crack dataset but also on the public datasets CRKWH100 and CrackLS315 to increase the credibility of the experiments. As shown in Table 4, CrackNet has the best metric performance on both the UAVRoadCrack dataset and the CRKWH100 and CrackLS315 datasets and shows a more significant improvement compared to all other networks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image Size** & **Batch Size** & **Epoch** & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Learning** \\ **Rate Decay** \\ \end{tabular} & \begin{tabular}{c} **Weight** \\ **Decay** \\ \end{tabular} & \
\begin{tabular}{c} **Dropout** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **MHSA** \\ **Layer Num** \\ \end{tabular} \\ \hline \(384\times 384\) & 8 & 100 & \(1\times 10^{-3}\) & 0.1 & 0.00001 & 0.1-0.5 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter settings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ODS** & **OIS** & **AP** \\ \hline Baseline & 0.601 & 0.613 & 0.608 \\ +MHDC & 0.623 & 0.626 & 0.624 \\ +MHDC + GE & 0.631 & 0.635 & 0.633 \\ +MHSA & 0.628 & 0.631 & 0.627 \\ +MHDC + MHSA & 0.652 & 0.663 & 0.651 \\ +MHDC + MHSA + GE & 0.661 & 0.672 & 0.665 \\ +MHDC + MHSA + GE + L315 & 0.673 & 0.688 & 0.682 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the ablation experiments. **MHDC**: multi-scale hybrid dilated convolution. **GE**: GN + ELU. **MHSA**: multi-head self-attention. **L315**: model pretrained on CrackL315. On the UAVRoadCrack dataset, CrackNet network engages 0.06, 0.059, and 0.057 over the ODS, OIS, and AP, respectively, of the baseline model, all of which confirm that the CrackNet network is more suitable for road crack detection on UAV aerial road remote sensing images than other segmentation networks. The FCN network has poor detection performance. The ResNet and SegNet networks have similar results for each evaluation metric. On the CRWH100 public dataset, the ODS, OSI, and AP of CrackNet reached 0.933, 0.936, and 0.942, respectively, showing varying degrees of improvement over other mainstream networks. On the CrackLS315 public dataset, the three metrics of the DeepCrack network training results were 0.845, 0.867, and 0.877, while the CrackNet network still outperformed each of them by an average of 0.017. These experimental results show that the CrackNet network is also suitable for crack detection in other scenarios and has good generalization ability. Figure 12 shows the detection results of different algorithms on the same UAV remotely sensed highway crack image. From the comparison of the detection results, it can be observed that CrackNet can detect more crack details and is closer to the ground truth than the other networks. Additionally, less environmental noise is also detected around the highway, indicating that the network also has better noise immunity. The confusion matrix for the accuracy of CrackNet on the UAVRoadCrack dataset is shown in Table 5. For real cracks, 68.3% were detected as cracks and 31.7% were detected as non-cracks; for real non-cracks, 66.6% were detected as non-cracks and 33.4% were detected as cracks. The confusion matrix for the accuracy of CrackNet on the CRWH100 dataset is shown in Table 6. For real cracks, 93.8% were detected as cracks and 6.2% were detected as non-cracks; for real non-cracks, 94.1% were detected as non-cracks and 5.9% were detected as cracks. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Predicted Results**} \\ \cline{2-3}
**Real Results** & **Cracks** & **Non-Cracks** \\ \hline Cracks & 93.8\% & 6.2\% \\ Non-Cracks & 5.9\% & 94.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The confusion matrix for the accuracy of CrackNet on the CRWH100 dataset.