# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 4 Conclusions  
Pavement distress was detected using deep learning and horizontal GPR images in this study. The anomalous waveform areas in horizontal radar images were located by a CP-YOLOX model. Five SViT models classified anomalous waveforms into one of the possible distress categories. A GPR image dataset collected from China demonstrated the effectiveness of the proposed models. The following conclusions can be drawn.  
1. The proposed CP-YOLOX model could localize anomalous waveforms caused by pavement distresses. With a confidence threshold of 0.5, the CP-YOLOX model localized anomalous waveforms with mAP of 80.64%, Precision of 87.71%, and Recall of 58.73%. The model processed radar images with a speed of 33.57 images/s.
2. The proposed SViT model was capable of detecting cracks, poor interlayer bonding, and mixture segregation distress in horizontal radar images. For the category without background noise, the model had a high prediction accuracy. Future studies should focus on how to suppress the noise waveform and on more detailed analysis of the disturbance waveform.
3. The proposed SViT model had fewer parameters and FLOPs, ensuring a high level of accuracy, which enables it to perform distress classification quickly. With the increase in GPR images, the gap between SViT and ResNet50 shrunk from 6.06% to 1%, indicating that more data samples had the potential to improve the performance of SViT. This demonstrated the superiority of the proposed model on the pavement distress classification.
4. In the three classification datasets, the 3-categories dataset had the highest accuracy, followed by the 4-categories dataset, and the 5-categories dataset had the lowest accuracy. However, the model trained based on the 5-categories dataset provided the most detailed basis for distress classification. Subsequently, we need to combine the horizontal detection results with the longitudinal radar images to determine the best classification method using 3D GPR.  
Conceptualization, J.Y.; methodology, K.R.; software, K.R.; validation, K.R. and J.G.; formal analysis, K.R.; investigation, S.Y. and L.Z.; resources, J.Y.; data curation, S.Y. and L.Z.; writing--original draft preparation, K.R.; writing--review and editing, K.R. and J.G.; visualization, K.R.; supervision, J.G.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Jiangxi Province of China (grant numbers 20202 BABL214046); Science and technology project of Jiangxi Provincial Department of Education (GJ)210623); Key R & D projects of Xinjiang Uygur Autonomous Region (Grant No. 2021B01005); Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102210501).  
Not applicable.  
All data, models, and code generated or used during the study appear in the submitted article.  
No conflicts of interest exist in the submission of this manuscript, and the manuscript is approved by all authors for publication. We would like to declare on behalf of my co-authors that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All authors listed have approved the manuscript that is enclosed.