Skip to main content
SpringerLink
Log in

PD-ITS: Pothole Detection Using YOLO Variants for Intelligent Transport System

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Every country has a significant problem with road connectivity in terms of development. The road damage assessment along particular thoroughfares has revealed that governmental efforts to preserve road quality whether through construction or maintenance are substantial. Potholes are typically the first thing noticed while assessing roadway quality in developing nations such as India. Intelligent Transportation Systems (ITS) have made significant strides in automation and computer vision compared to other methods such as radar, sensor bases, manual methods, etc. Recent research has shown that intelligent transport systems perform exceptionally well, especially in pothole detection and assessment. Recent advances in artificial intelligence, particularly machine learning and deep learning, have advanced robotics and automation. Modern technology has produced better results in production and cost-efficiency than traditional methods. The paper emphasizes the need for better road maintenance to decrease accidents caused by potholes on Indian roadways. Intelligent Transportation Systems (ITS) face road abnormalities beyond road damages that pose safety issues. Detecting and managing surface fissures, potholes, road signs, landslides, and animal crossings are the concerns. Deep learning and artificial intelligence can improve Intelligent Transportation Systems by providing a holistic approach to road concerns. Technology should make transportation networks safer and more efficient. The research aims to examine the effectiveness of three deep-learning object identification frameworks (YOLOv5, YOLOv6, and YOLOv7) in detecting potholes. The results demonstrate that deep learning methods are highly effective for identifying road damage within the Intelligent Transport System, especially potholes. The dataset included in this study consists of photographs depicting potholes observed on diverse categories of roadways, namely municipal, state, and national highways. The empirical findings indicate that YOLOv7 has superior efficiency as a pothole detector, with a precision rate of 93%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data Availability

All the data and materials are available to the authors.

References

  1. NHAI National High way Authority of India (NHAI). https://nhai.gov.in

  2. The Economic Times. Supreme Court takes note of 3,597 deaths due to pothole-related accidents in 2017 - The Economic Times. Econ Times; 2018.

    Google Scholar 

  3. The Indian Express. Union transport minister nitin gadkari inaugurates CRRI’ s pothole repair machine. The Indian Express; 2022.

    Google Scholar 

  4. Patel S. 2022. Pothole image data-set, Kaggle. https://www.kaggle.com/datasets/sachinpatel21/pothole-image-dataset. Accessed 20 Aug 2022.

  5. Colagrande S, Ranalli D, Tallini M. GPR research on damaged road pavements built in cut and fill sections. Transp Res Procedia. 2020;45:30–7. https://doi.org/10.1016/j.trpro.2020.02.059.

    Article  Google Scholar 

  6. Ravi R, Bullock D, Habib A. Highway and airport runway pavement inspection using mobile lidar. Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch. 2020;43:349–54. https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-349-2020.

    Article  Google Scholar 

  7. Koch C, Brilakis I. Pothole detection in asphalt pavement images. Adv Eng Inf. 2011;25:507–15. https://doi.org/10.1016/j.aei.2011.01.002.

    Article  Google Scholar 

  8. Ozoglu F, Gökgöz T. Detection of road potholes by applying convolutional neural network method based on road vibration data. Sensors (Basel). 2023. https://doi.org/10.3390/s23229023.

    Article  Google Scholar 

  9. Dhall R, Solanki VK. An IoT based predictive connected car maintenance approach. Int J Interact Multimed Artif Intell. 2017;4:16. https://doi.org/10.9781/ijimai.2017.433.

    Article  Google Scholar 

  10. You C, Wen C, Luo H, Wang CLJ. Rapid traffic damage inspection in natural scenes using mobile laser scanning data. IEEE.; 2017. p. 62271–6274.

    Google Scholar 

  11. Ikidid A, El FA, Sadgal M. Multi-agent and fuzzy inference-based framework for traffic light optimization. Int J Interact Multimed Artif Intell. 2023;8:88–97. https://doi.org/10.9781/ijimai.2021.12.002.

    Article  Google Scholar 

  12. Nienaber S, Booysen MJ. Detecting potholes using image processing techniques and real-world footage. Singapore: Springer Nature; 2021. p. 893–902.

    Google Scholar 

  13. Omar M, Kumar P. Image features selection based on computer vision techniques to detect potholes for intelligent transport system. Int Conf Inf Sci Commun Technol Appl Trends Oppor ICISCT. 2021. https://doi.org/10.1109/ICISCT52966.2021.9670066.

    Article  Google Scholar 

  14. Bibi R, Saeed Y, Zeb A, et al. Edge ai-based automated detection and classification of road anomalies in vanet using deep learning. Comput Intell Neurosci. 2021. https://doi.org/10.1155/2021/6262194.

    Article  Google Scholar 

  15. Guo L, Ge PS, Zhang MH, et al. Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine. Expert Syst Appl. 2012;39:4274–86. https://doi.org/10.1016/j.eswa.2011.09.106.

    Article  Google Scholar 

  16. Zhang Z, Ai X, Chan CK, Dahnoun N. An efficient algorithm for pothole detection using stereo vision ICASSP Int Conf Acoust Speech Signal Process Proc. IEEE; 2014. p. 564–8.

    Google Scholar 

  17. Rajagopal A, Ramachandran A, Shankar K, et al. Fine-tuned residual network-based features with latent variable support vector machine-based optimal scene classification model for unmanned aerial vehicles. IEEE Access. 2020;8:118396–404. https://doi.org/10.1109/ACCESS.2020.3004233.

    Article  Google Scholar 

  18. Maeda H, Kashiyama T, Sekimoto Y, et al. Generative adversarial network for road damage detection. Comput Civ Infrastruct Eng. 2021;36:47–60. https://doi.org/10.1111/mice.12561.

    Article  Google Scholar 

  19. Wang YJ, Ding M, Kan S, et al. Deep proposal and detection networks for road damage detection and classification, 2018 IEEE international conference on big data (Big data). USA: IEEE; 2018. p. 5224–7.

    Google Scholar 

  20. Gajjar K, Van NT, Wilm T, Mercorelli P. Vision-based deep learning algorithm for detecting potholes. J Phys Conf Ser. 2022. https://doi.org/10.1088/1742-6596/2162/1/012019.

    Article  Google Scholar 

  21. Zhang H, Wang Y, Liu Y. IFD : an intelligent fast detection for real-time image information in industrial IoT. Appl Sci. 2022;12(15):7847.

    Article  Google Scholar 

  22. Asad MH, Khaliq S, Yousaf MH, et al. Pothole detection using deep learning: a real-time and ai-on-the-edge perspective. Adv Civ Eng. 2022. https://doi.org/10.1155/2022/9221211.

    Article  Google Scholar 

  23. Rajagopal A, Joshi GP, Ramachandran A, et al. A deep learning model based on multi-objective particle swarm optimization for scene classification in unmanned aerial vehicles. IEEE Access. 2020;8:135383–93. https://doi.org/10.1109/ACCESS.2020.3011502.

    Article  Google Scholar 

  24. Pillai MS, Chaudhary G, Khari M, Crespo RG. Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning. Soft Comput. 2021;25:11929–40. https://doi.org/10.1007/s00500-021-05576-w.

    Article  Google Scholar 

  25. Heo DH, Choi JY, Kim SB, et al. Image-based pothole detection using multi-scale feature network and risk assessment. Electron. 2023. https://doi.org/10.3390/electronics12040826.

    Article  Google Scholar 

  26. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once Unified, real-time object detection. Proc Comput Soc Conf Comput Vis Pattern Recognit 2016-Decem. IEEE; 2016.

    Google Scholar 

  27. Redmon J, Farhadi A. Yolo V2.0. Cvpr. 2016;2017:187–213.

    Google Scholar 

  28. Redmon J, Farhadi A. YOLOv3: an incremental improvement. arXiv preprint. 2018. arXiv: 1804.02767.

  29. Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: optimal speed and accuracy of object detection. Comput Vis Pattern Recognit arXiv. 2020;2004:1–17.

    Google Scholar 

  30. Omar M, Kumar P. Detection of roads potholes using YOLOv4 2020. Int Conf Inf Sci Commun Technol ICISCT. IEEE; 2020.

    Google Scholar 

  31. Li S, Li Y, Li Y, et al. YOLO-FIRI: Improved YOLOv5 for infrared image object detection. IEEE Access. 2021;9:141861–75. https://doi.org/10.1109/ACCESS.2021.3120870.

    Article  Google Scholar 

  32. Li C et al. YOLOv6: a single-stage object detection framework for industrial applications. 2022. arXiv: 2209.02976.

  33. Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. IEEE; 2022.

    Google Scholar 

  34. Ahmed KR. Smart pothole detection using deep learning based on dilated convolution. Sensors. 2021;21(24):8406. https://doi.org/10.3390/s21248406.

    Article  Google Scholar 

Download references

Funding

No funding is provided for this research.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, Maulana Azad National Urdu University, Hyderabad, India

    Mohd Omar & Pradeep Kumar

Authors
  1. Mohd Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mohd Omar: Implementation, Writing—original draft, Pradeep Kumar: Suggestion, Proofreading.

Corresponding author

Correspondence to Mohd Omar.

Ethics declarations

Conflict of Interest

The authors declare that they do not have any competing interests.

Research Involving Human and/or Animals

No involvement of human and animals for this research.

Informed Consent

The authors give their consent to publish this original research work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Security for Communication and Computing Application” guest edited by Karan Singh, Ali Ahmadian, Ahmed Mohamed Aziz Ismail, R S Yadav, Md. Akbar Hossain, D. K. Lobiyal, Mohamed Abdel-Basset, Soheil Salahshour, Anura P. Jayasumana, Satya P. Singh, Walid Osamy, Mehdi Salimi and Norazak Senu.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Omar, M., Kumar, P. PD-ITS: Pothole Detection Using YOLO Variants for Intelligent Transport System. SN COMPUT. SCI. 5, 552 (2024). https://doi.org/10.1007/s42979-024-02887-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-02887-1

Keywords

Search

Navigation