## 5 Conclusions  
The proposed scheme for UAV remote sensing image-based highway crack detection and classification in this paper uses a professional surveying and mapping UAV to collect highway data. A series of environmental noise removal operations were performed on the collected highway data, including creating a roadbed dataset, identifying the roadbed, removing the central yellow line, obtaining roadbed pixel coordinates, and removing environmental noise from the full-size image, resulting in an aerial image of only the highway surface. Then, the CrackNet model, trained on the UAVRoadCrack dataset, was used for crack detection on the highway surface images. Finally, the CrackClassification algorithm was applied to the crack detection result image of the highway surface for crack classification. The main contributions of the paper are as follows:  
(1) Creation of a UAV remote sensing road crack dataset, UAVRoadCrack, which fills the gap in the field and facilitates further research.  
(2) Proposal of a novel network for UAV remote sensing road crack detection, CrackNet, which uses a multi-scale hybrid dilated convolution (MHDC) module to obtain the global features and multi-scale contextual information of cracks, a multi-head self-attention (MHSA) module to capture the detailed features of the cracks, and group normalization instead of batch normalization to reduce training batch errors. The CrackNet achieved an AP of 0.665, 0.942, and 0.895 on the UAVRoadCrack, CRKWH100, and CrackLS315 datasets, respectively, which is better than for other mainstream crack detection networks. Other evaluation metrics also outperformed other mainstream crack detection algorithms, demonstrating the better generalization and robustness of the CrackNet algorithm.  
(3) A preprocessing operation and CrackClassification algorithm were created for crack detection result maps based on roadbed identification, which can effectively exclude environmental noise and classify complex cracks. Compared to directly detecting cracks on full-size images and then removing environmental noise, this approach saves about 65% of GPU resources. The precision for horizontal, vertical, block, and reticulated cracks is 0.839, 0.867, 0.740, and 0.778, respectively. The recall for horizontal, vertical, block, and reticulated cracks is 0.868, 0.849, 0.738, and 0.772, respectively. These results confirm the scientific and rational nature of the classification scheme and algorithm used in this study.  
Overall, in this paper, a comprehensive solution is proposed for UAV remote sensing road crack detection and classification, which can significantly improve the efficiency and accuracy of road crack detection and classification. In the future, we can use generative adversarial networks (GANs) to fuse images and remove duplicate areas in consecutive captured images that can then be used to augment the training data and improve the network performance.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z.; formal analysis, G.S., L.Z., X.W. and F.W.; investigation, G.S.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., L.Z., X.W., F.W. and G.S.; visualization, Y.Z.; supervision, G.S.; project administration, G.S. and Y.Z.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China No.62162059 and supported by the Third Xinjiang Scientific Expedition Program under Grant No.2021 xjkk1400.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Precision** & **Recall** \\ \hline Transverse Cracks & 0.839 & 0.868 \\ Longitudinal Cracks & 0.867 & 0.849 \\ Block Cracks & 0.740 & 0.738 \\ Reticulated Cracks & 0.778 & 0.772 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The precision and recall for four types of cracks.