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YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism

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Abstract

Maintaining road pavement integrity is crucial for ensuring safe and efficient transportation. Conventional methods for assessing pavement condition are often laborious and susceptible to human error. This paper proposes YOLO9tr, a novel lightweight object detection model for pavement damage detection, leveraging the advancements of deep learning. YOLO9tr is based on the YOLOv9 architecture, incorporating a partial attention block that enhances feature extraction and attention mechanisms, leading to improved detection performance in complex scenarios. The model is trained on a comprehensive dataset comprising road damage images from multiple countries. This dataset includes an expanded set of damage categories beyond the standard four types (longitudinal cracks, transverse cracks, alligator cracks, and potholes), providing a more nuanced classification of road damage. This broadened classification range allows for a more accurate and realistic assessment of pavement conditions. Comparative analysis demonstrates YOLO9tr’s superior precision and inference speed compared to state-of-the-art models like YOLOv8, YOLOv9 and YOLOv10, achieving a balance between computational efficiency and detection accuracy. The model achieves a high frame rate of up to 136 FPS, making it suitable for real-time applications such as video surveillance and automated inspection systems. The research presents an ablation study to analyze the impact of architectural modifications and hyperparameter variations on model performance, further validating the effectiveness of the partial attention block. The results highlight YOLO9tr’s potential for practical deployment in real-time pavement condition monitoring, contributing to the development of robust and efficient solutions for maintaining safe and functional road infrastructure.

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Data availability

The GitHub repository for the model and trained weights is available at https://github.com/Sompote/YOLO9tr.

References

  1. Ibragimov, E., Kim, Y., Lee, J.H., Cho, J., Lee, J.-J.: Automated pavement condition index assessment with deep learning and image analysis: an end-to-end approach. Sensors 24, 2333 (2024). https://doi.org/10.3390/s24072333

    Article  Google Scholar 

  2. Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. arXiv:1311.2524 (2014)

  3. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)

  4. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 779–788. IEEE, Las Vegas, NV, USA (2016)

  5. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7263–7271 (2017)

  6. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  7. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: optimal speed and accuracy of object detection. CoRR. abs/2004.10934 (2020)

  8. Ultralytics: Ultralytics/yolov5: v7.0–YOLOv5 SOTA realtime instance segmentation. 10.5281/zenodo.7347926 (2022)

  9. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: a single-stage object detection framework for industrial applications (2022)

  10. Wang, C.-Y., Liao, H.-Y.M.: YOLOv9: learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616 (2024)

  11. Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: YOLOv10: real-time end-to-end object detection (2024)

  12. Zhong, J., Zhu, J., Huyan, J., Ma, T., Zhang, W.: Multi-scale feature fusion network for pixel-level pavement distress detection. Autom. Constr. 141, 104436 (2022). https://doi.org/10.1016/j.autcon.2022.104436

    Article  Google Scholar 

  13. Zhong, J., Huyan, J., Zhang, W., Cheng, H., Zhang, J., Tong, Z., Jiang, X., Huang, B.: A deeper generative adversarial network for grooved cement concrete pavement crack detection. Eng. Appl. Artif. Intell. 119, 105808 (2023). https://doi.org/10.1016/j.engappai.2022.105808

    Article  Google Scholar 

  14. Zhong, J., Zhang, M., Ma, Y., Xiao, R., Cheng, G., Huang, B.: A multitask fusion network for region-level and pixel-level pavement distress detection. J. Transp. Eng. Part B Pavements 150, 04024002 (2024). https://doi.org/10.1061/JPEODX.PVENG-1433

    Article  Google Scholar 

  15. Zhu, J., Zhong, J., Ma, T., Huang, X., Zhang, W., Zhou, Y.: Pavement distress detection using convolutional neural networks with images captured via UAV. Autom. Constr. 133, 103991 (2022). https://doi.org/10.1016/j.autcon.2021.103991

    Article  Google Scholar 

  16. Zhong, J., Ma, Y., Zhang, M., Xiao, R., Cheng, G., Huang, B.: A Pavement crack translator for data augmentation and pixel-level detection based on weakly supervised learning. IEEE Trans. Intell. Transport. Syst. (2024). https://doi.org/10.1109/TITS.2024.3402110

    Article  Google Scholar 

  17. Arya, D., Maeda, H., Ghosh, S.K., Toshniwal, D., Sekimoto, Y.: RDD2022: a multi-national image dataset for automatic road damage detection. https://arxiv.org/abs/2209.08538 (2022)

  18. Caltagirone, L., Bellone, M., Svensson, L., Wahde, M.: A deep learning approach for road damage classification. In: International Conference on Intelligent Computing. pp. 1017–1026. Springer (2019)

  19. Yu, G., Zhou, X.: An improved YOLOv5 crack detection method combined with a bottleneck transformer. Mathematics. 11, 2377 (2023). https://doi.org/10.3390/math11102377

    Article  Google Scholar 

  20. Wang, X., Gao, H., Jia, Z., Li, Z.: BL-YOLOv8: an improved road defect detection model based on YOLOv8. Sensors 23, 8361 (2023). https://doi.org/10.3390/s23208361

    Article  Google Scholar 

  21. As Sami, A., Sakib, S., Deb, K., Sarker, I.H.: Improved YOLOv5-based real-time road pavement damage detection in road infrastructure management. Algorithms 16, 452 (2023). https://doi.org/10.3390/a16090452

    Article  Google Scholar 

  22. Zhao, M., Su, Y., Wang, J., Liu, X., Wang, K., Liu, Z., Liu, M., Guo, Z.: MED-YOLOv8s: a new real-time road crack, pothole, and patch detection model. J. Real-Time Image Proc. 21, 26 (2024). https://doi.org/10.1007/s11554-023-01405-5

    Article  Google Scholar 

  23. Guo, G., Zhang, Z.: Road damage detection algorithm for improved YOLOv5. Sci. Rep. 12, 15523 (2022). https://doi.org/10.1038/s41598-022-19674-8

    Article  Google Scholar 

  24. Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H.: Road damage detection using deep neural networks with images captured through a smartphone. Comput. Aided Civ. Infrastruct. Eng. (2018). https://doi.org/10.1111/mice.12387

    Article  Google Scholar 

  25. Pham, V., Nguyen, D., Donan, C.: Road damages detection and classification with YOLOv7 (2022)

  26. Rent GPUs | Vast.ai. https://vast.ai/, https://vast.ai/

  27. Wong, K.-Y.: WongKinYiu/yolov9. https://github.com/WongKinYiu/yolov9 (2024)

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Authors and Affiliations

  1. AI Research Group, Department of Civil Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Sompote Youwai & Achitaphon Chaiyaphat

  2. Infraplus Co., Ltd., Bangkok, Thailand

    Pawarotorn Chaipetch

Authors
  1. Sompote Youwai
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  2. Achitaphon Chaiyaphat
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  3. Pawarotorn Chaipetch
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Contributions

Author Contributions: The study’s conception and design were collaboratively developed by all authors. Sompote Youwai was responsible for writing the initial draft, conceptualizing the study, developing the software, and validating the results. Achitaphon Chaiyaphat conducted the investigation, prepared the data, trained the model, and managed the labeling process. Pawarotorn Chaipetch contributed to data preparation and labeling tasks.

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Correspondence to Sompote Youwai.

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Youwai, S., Chaiyaphat, A. & Chaipetch, P. YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism. J Real-Time Image Proc 21, 163 (2024). https://doi.org/10.1007/s11554-024-01545-2

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  • DOI: https://doi.org/10.1007/s11554-024-01545-2

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