Efficient Road Navigation with Deep Learning on Damaged Routes | IJCT Volume 13 – Issue 3 | IJCT-V13I3P96
International Journal of Computer Techniques
ISSN 2394-2231
Author
R. Anto Pravin, A. Sai Prashanth, D. Revanth Kumar, M. Naga Sujan
Abstract
The increase of potholes and road surface damage poses important challenges to traffic safety and urban infrastructure management. Traditional detection methods rely on either manual inspection or cloud-based processing, which has more latency, cost, and scalability limitations. This paper proposes a real-time deep learning system for efficient road navigation using YOLOv4-tiny for pothole detection. The model classifies detected potholes based on its severity like small, medium, large and suggests the optimal navigation directions like left, center, right based on severity density in each region of the frame. The system integrates a web user interface built with Flask, which enables users to upload images or videos or use real-time webcam feeds for detection. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to optimize model training. Evaluation metrics including Damage Density (DD), Route Efficiency Score (RES), and F1 Score validate the system’s performance. The proposed solution achieves high detection precision and recall, ensures low-latency inference without requiring cloud connectivity, and demonstrates significant improvements in road safety decision- making. By integrating real-time detection with visual analytics and direction guidance, the system act as a tool for traffic management and preventive road maintenance.
Keywords
Deep learning, Artificial Intelligence, Road Damage Detection, Cloud Computing, Computer Vision, Data Transmission, Road Navigation.
Conclusion
Efficient Road Navigation is capable of efficiently enhancing road damage detection accuracy and navigation through deep learning techniques, i.e., the YOLO object detection framework. Through optimizing dataset preprocessing, utilization of augmentation techniques, and improving the ability of the model to extract features, Efficient Road Navigation has been able to significantly enhance detection accuracy and recall. Additionally, the use of real- time video capture and processing modules ensures the system is in a position to evaluate road conditions in real-time, thus making it a very effective and trustworthy solution.
Unlike previous models current approach minimizes latency by the use of edge computing which helps in achieving faster and better real-time detection. This technology offers responsive and smooth user interface, thus rendering the system flexible and scalable to different road conditions. In addition, the presence of the API facilitates secure data sharing with navigation and traffic observation services, thus enhancing road safety in general. The live deployment of Efficient Road Navigation model has shown robust reliability in detecting road hazards and low false positive rates. The performance of the system is also added by metrics like Damage Density, Route Efficiency Score, and F1 Score, which show robust improvements over existing methods. A solution, through the combination of deep learning methods with smart navigation, offers a robust and viable solution to road safety and maintenance enhancement.
References
[1]D. Arya, H. Maeda, S.K. Ghosh, D. Toshniwal, H. Omata, T. Kashiyama, and Y. & Sekimoto, “Global road damage detection: State- of-the-art solutions”, In IEEE International Conference on Big Data, pp. 5533-5539, 2020.
[2]H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, H., and Omata, “Road damage detection using deep neural networks with images captured through a smartphone”, arXiv preprint arXiv:1801.09454, 2018.
[3]X. Song, C. Ren, H. Jiang, L. Li, W. Wu, L. Li, and J. Wu, “Enhanced Map‐Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons”, Mathematical Problems in Engineering, 2022(1), 7650435, 2022.
[4]A. Aboutaleb, A.S. El-Wakeel, H. Elghamrawy, and A. Noureldin,
“Lidar/riss/gnss dynamic integration for land vehicle robust positioning in challenging gnss environments”, Remote Sensing, 12(14), 2323, 2020.
[5]A. Abosekeen, A. Noureldin, and M.J Korenberg, “Improving the RISS/GNSS land-vehicles integrated navigation system using magnetic azimuth updates”, IEEE Transactions on Intelligent Transportation Systems, 21(3), 1250-1263, 2019.
[6]M. Hentschel, and B. Wagner, “Autonomous robot navigation based on openstreetmap geodata”, In 13th International IEEE Conference on Intelligent Transportation Systems, pp. 1645-1650, 2010.
[7]T. Ort, L. Paull, and D. Rus, “Autonomous vehicle navigation in rural environments without detailed prior maps”, In IEEE international conference on robotics and automation (ICRA), pp. 2040-2047, 2018.
[8]G. Zhang, N. Zheng, C. Cui, Y. Yan, and Z. Yuan, “An efficient road detection method in noisy urban environment”, In IEEE Intelligent Vehicles Symposium, pp. 556-561, 2009.
[9]L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast LIDAR-based road detection using fully convolutional neural networks”, In IEEE intelligent vehicles symposium (iv), pp. 1019- 1024, 2017.
[10]Z. Chen, J. Zhang, and D. Tao, “Progressive lidar adaptation for road detection”, IEEE/CAA Journal of Automatica Sinica, 6(3), 693-702, 2019.
[11]V. Mandal, A.R. Mussah, and Y. Adu-Gyamfi, “Deep learning frameworks for pavement distress classification: A comparative analysis”, In IEEE International Conference on Big Data (Big Data), pp. 5577-5583, 2020.
[12]M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering”, Advances in neural information processing systems, 29, 2016.
[13]G. Floros, B. Van Der Zander, and B. Leibe, “Openstreetslam: Global vehicle localization using openstreetmaps”, In IEEE international conference on robotics and automation, pp. 1054-1059, 2013.
[14]L.A. Silva, V.R.Q. Leithardt, V.F.L. Batista, G.V. González, J.F.D.P. Santana, “Automated road damage detection using UAV images and deep learning techniques”, IEEE Access, 11, 62918-62931, 2023.
[15]M. Ren, X. Zhang, X. Chen, B. Zhou, and Z. Feng, “YOLOv5s-M: A deep learning network model for road pavement damage detection from urban street-view imagery”, International Journal of Applied Earth Observation and Geoinformation, 120, 103335, 2023.
[16]C.C Hsieh, H.W. Jia, W.H. Huang, and M.H. Hsih, “Deep Learning- Based Road Pavement Inspection by Integrating Visual Information and IMU”, Information, 15(4), 239, 2024.
[17]A.P. Botezatu, A. Burlacu, and C. Orhei, “A review of deep learning advancements in road analysis for autonomous driving”, Applied Sciences, 14(11), 4705, 2024.
How to Cite This Paper
R. Anto Pravin, A. Sai Prashanth, D. Revanth Kumar, M. Naga Sujan (2026). Efficient Road Navigation with Deep Learning on Damaged Routes. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
Related Posts:
