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PDSE-YOLOv8: a lightweight detection method for internal defects in asphalt roads

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Abstract

Ground penetrating radar (GPR) is an effective tool for detecting internal defects in asphalt roads due to its non-destructive nature and high resolution. However, detecting defects in GPR images remains challenging, as existing models lack sufficient accuracy and are often complex and redundant. To address these issues, a lightweight real-time detection method based on ground-penetrating radar is proposed in this study. First, a field GPR image dataset of asphalt roads was collected and constructed. To address the limited defect sample data acquired by GPR, an efficient copy-and-paste augmentation method was employed. This method involved copying and pasting defect samples in GPR images while incorporating random scale jitter and position migration operations to generate a sufficient number of real defect samples. Second, the C2f-DSConv module and the SE attention mechanism were designed and introduced based on the YOLOv8 network to improve detection accuracy in the complex background environment of GPR images. Finally, a channel pruning strategy was used to prune the improved YOLOv8 network, reducing model complexity while maintaining detection accuracy. The final model achieves an average detection accuracy of 90.9% and a detection speed of 140.9 FPS. The results show that the proposed method combines both detection accuracy and real-time performance, further advancing the engineering application of internal defect detection in asphalt roads.

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Data availability

Data will be made available on request. No datasets were generated or analysed during the current study.

References

  1. Xiong, C., Yu, J., Zhang, X.J.: Use of NDT systems to investigate pavement reconstruction needs and improve maintenance treatment decision-making. Int. J. Pavement Eng. 24, 2011872 (2023)

    Article  Google Scholar 

  2. He, J., Wang, Y., Wang, Y., Li, R., Zhang, D., Zheng, Z.: A lightweight road crack detection algorithm based on improved YOLOv7 model. SIViP 18, 847–860 (2024). https://doi.org/10.1007/s11760-024-03197-y

    Article  Google Scholar 

  3. Rasol, M.A., Pérez-Gracia, V., Fernandes, F.M., Pais, J.C., Santos-Assunçao, S., Santos, C., Sossa, V.: GPR laboratory tests and numerical models to characterize cracks in cement concrete specimens, exemplifying damage in rigid pavement. Measurement 158, 107662 (2020). https://doi.org/10.1016/j.measurement.2020.107662

    Article  Google Scholar 

  4. Wang, S., Zhao, S., Al-Qadi, I.L.J.: Real-time density and thickness estimation of thin asphalt pavement overlay during compaction using ground penetrating radar data. Surv. Geophys. 41, 431–445 (2020). https://doi.org/10.1007/s10712-019-09556-6

    Article  Google Scholar 

  5. Maruyama, K., Kumagai, M.J.: Evaluation of fatigue damage in asphalt pavement using FWD dissipated work. J. Jpn. Soc. Civ. Eng. 67, I27–I34 (2011)

    Google Scholar 

  6. Cheng, Y., Shi, Z.J.: Permanent deformation and temperature monitoring of subgrades using fiber Bragg grating sensing technology. J. Sens. 2021, 8824058 (2021). https://doi.org/10.1155/2021/8824058

    Article  Google Scholar 

  7. Pan, W.-H., Sun, X.-D., Wu, L.-M., Yang, K.-K., Tang, N.J.M.: Damage detection of asphalt concrete using piezo-ultrasonic wave technology. Materials 12, 443 (2019). https://doi.org/10.3390/ma12030443

    Article  Google Scholar 

  8. Kulkarni, N.N., Dabetwar, S., Benoit, J., Yu, T., Sabato, A.: Comparative analysis of infrared thermography processing techniques for roadways’ sub-pavement voids detection. NDT E Int. 129, 102652 (2022). https://doi.org/10.1016/j.ndteint.2022.102652

    Article  Google Scholar 

  9. Kim, D.-H., Choi, M.-K., Han, S.-H., Jeong, J.-H.J.S.: Determination of partial depth repair size for spalling of jointed concrete pavements using the impact echo method. Sustainability 14, 8143 (2022). https://doi.org/10.3390/su14138143

    Article  Google Scholar 

  10. Liu, W., Luo, R., Xiao, M., Chen, Y.: Intelligent detection of hidden distresses in asphalt pavement based on GPR and deep learning algorithm. Constr. Build. Mater. 416, 135089 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135089

    Article  Google Scholar 

  11. Zhao, S., Al-Qadi, I.L.: Development of regularization methods on simulated ground-penetrating radar signals to predict thin asphalt overlay thickness. Signal Process. 132, 261–271 (2017). https://doi.org/10.1016/j.sigpro.2016.06.015

    Article  Google Scholar 

  12. Saarenketo, T., Scullion, T.: Road evaluation with ground penetrating radar. J. Appl. Geophys. 43, 119–138 (2000). https://doi.org/10.1016/S0926-9851(99)00052-X

    Article  Google Scholar 

  13. Liu, Z., Gu, X., Wu, W., Zou, X., Dong, Q., Wang, L.: GPR-based detection of internal cracks in asphalt pavement: a combination method of DeepAugment data and object detection. Measurement 197, 111281 (2022). https://doi.org/10.1016/j.measurement.2022.111281

    Article  Google Scholar 

  14. Solla, M., Pérez-Gracia, V., Fontul, S.: A review of GPR application on transport infrastructures: troubleshooting and best practices. Remote Sens. (2021). https://doi.org/10.3390/rs13040672

    Article  Google Scholar 

  15. Liu, C., Du, Y., Yue, G., Li, Y., Wu, D., Li, F.: Advances in automatic identification of road subsurface distress using ground penetrating radar: state of the art and future trends. Autom. Constr. 158, 105185 (2024). https://doi.org/10.1016/j.autcon.2023.105185

    Article  Google Scholar 

  16. Shahrabi, M.A., Hashemi, H.J.: Analysis of GPR hyperbola targets using image processing techniques. J. Seism. Explor. 6, 561–575 (2021)

    Google Scholar 

  17. Dong, Z., Ye, S., Gao, Y., Fang, G., Zhang, X., Xue, Z., Zhang, T.: Rapid detection methods for asphalt pavement thicknesses and defects by a vehicle-mounted ground penetrating radar (GPR) System. Sensors (2016). https://doi.org/10.3390/s16122067

    Article  Google Scholar 

  18. Maas, C., Schmalzl, J.: Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Comput. Geosci. 58, 116–125 (2013). https://doi.org/10.1016/j.cageo.2013.04.012

    Article  Google Scholar 

  19. Todkar, S.S., Bastard, C.L., Ihamouten, A., Baltazart, V., Dérobert, X., Fauchard, C., Guilbert, D., Bosc, F.: Detection of debondings with Ground Penetrating Radar using a machine learning method. In: 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), pp. 1–6 (2017)

  20. Harkat, H., Ruano, A.E., Ruano, M.G., Bennani, S.D.: GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm. Appl. Soft Comput. 79, 310–325 (2019). https://doi.org/10.1016/j.asoc.2019.03.030

    Article  Google Scholar 

  21. Liu, C., Yao, Y., Li, J., Qian, J., Liu, L.: Research on lightweight GPR road surface disease image recognition and data expansion algorithm based on YOLO and GAN. Case Stud. Constr. Mater. 20, e02779 (2024). https://doi.org/10.1016/j.cscm.2023.e02779

    Article  Google Scholar 

  22. Liu, P., Wang, Q.: SCA-YOLOv4: you only look once with squeeze-and-excitation, coordinate attention and adaptively spatial feature fusion. Signal Image Video Process. (2024). https://doi.org/10.1007/s11760-024-03378-9

    Article  Google Scholar 

  23. Jiang, J., Li, P., Wang, J., Chen, H., Zhang, T.J.: Asphalt pavement crack detection based on infrared thermography and deep learning. Int. J. Pavement Eng. 25, 2295906 (2024). https://doi.org/10.1080/10298436.2023.2295906

    Article  Google Scholar 

  24. Tong, Z., Yuan, D., Gao, J., Wei, Y., Dou, H.: Pavement-distress detection using ground-penetrating radar and network in networks. Constr. Build. Mater. 233, 117352 (2020). https://doi.org/10.1016/j.conbuildmat.2019.117352

    Article  Google Scholar 

  25. Dou, Y.-T., Dong, G.-Q., Li, X.: Automatic identification of GPR targets on roads based on CNN and Grad-CAM. Appl. Geophys. (2024). https://doi.org/10.1007/s11770-024-1105-8

    Article  Google Scholar 

  26. Gao, J., Yuan, D., Tong, Z., Yang, J., Yu, D.: Autonomous pavement distress detection using ground penetrating radar and region-based deep learning. Measurement 164, 108077 (2020). https://doi.org/10.1016/j.measurement.2020.108077

    Article  Google Scholar 

  27. Ghiasi, G., Cui, Y., Srinivas, A., Qian, R., Lin, T.-Y., Cubuk, E.D., Le, Q.V., Zoph, B.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918–2928 (2021)

  28. Zhou, C., Ning, K., Wang, H., Yu, Z., Zhou, S., Bu, J.J.: Multi-view Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR. arXiv preprint arXiv: 230804779 (2023). https://doi.org/10.48550/arXiv.2308.04779

  29. Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6070–6079 (2023)

  30. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  31. Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736–2744 (2017)

  32. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, PMLR, pp. 448–456 (2015)

  33. Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv: 210708430 (2021). https://doi.org/10.48550/arXiv.2107.08430

  34. He, X., Tang, Z., Deng, Y., Zhou, G., Wang, Y., Li, L.: UAV-based road crack object-detection algorithm. Autom. Constr. 154, 105014 (2023). https://doi.org/10.1016/j.autcon.2023.105014

    Article  Google Scholar 

  35. Du, W., Jia, Z., Sui, S., Liu, P.: Table grape inflorescence detection and clamping point localisation based on channel pruned YOLOV7-TP. Biosyst. Eng. 235, 100–115 (2023). https://doi.org/10.1016/j.biosystemseng.2023.09.014

    Article  Google Scholar 

  36. Fan, S., Liang, X., Huang, W., JialongZhang, V., Pang, Q., He, X., Li, L., Zhang, C.: Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network. Comput. Electron. Agric. 193, 106715 (2022). https://doi.org/10.1016/j.compag.2022.106715

    Article  Google Scholar 

Download references

Funding

This project was supported by Natural Science Foundation of Qingdao Municipality (23-2-1-208-zyyd-jch).

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NL, JW, and ZL wrote the main manuscript text and prepared some of the figures and tables. WZ, KL undertook the data work. FZ prepared some of the figures and tables. All authors reviewed the manuscript.

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Correspondence to Junjie Wang.

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The authors have no financial or proprietary interests in any material discussed in this article. The authors declare no competing interests.

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Li, N., Zhang, W., Liu, Z. et al. PDSE-YOLOv8: a lightweight detection method for internal defects in asphalt roads. SIViP 18, 8925–8936 (2024). https://doi.org/10.1007/s11760-024-03518-1

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