Automated Detection of Road Defects for Driving Safety

This paper presented an automated road defect detection framework integrating CNN classification and YOLOv8 real-time object detection for identifying potholes and bumps across diverse road environments. Validated on a 12,000-image multi- scenario dataset using five-fold cross-validation, the YOLOv8 model achieved 96.7% accuracy and 94.8% [email protected] at 47.2 FPS, demonstrating strong suitability for real-time vehicle-mounted deployment. The end-to-end pipeline — spanning sensor data acquisition, GPU inference, geo-tagging, and cloud-based maintenance reporting — provides a practical, scalable solution for modern road asset management.   The integration of geo-tagging, severity scoring, and automated alerting enables road authorities to transition from reactive to proactive maintenance strategies, reducing the time between defect occurrence and repair. This directly translates into improved driving safety, reduced vehicle operating costs, and lower long-term infrastructure expenditure. The cross-scenario validation confirms that the framework generalizes well beyond controlled laboratory environments, making it suitable for real- world deployment across varied geographic and climatic conditions.   Future work will explore several directions. First, domain-adaptive training strategies will be investigated to improve construction-zone performance, potentially leveraging synthetic data augmentation via generative adversarial networks (GANs). Second, lightweight model distillation techniques will be developed for deployment on cost-constrained microcontroller-based edge nodes. Third, the detection scope will be extended to include additional defect categories such as surface cracks, rutting, alligator cracking, and lane-marking degradation. Finally, integration with smart city infrastructure and Vehicle-to-Everything (V2X) communication protocols will be explored to enable cooperative, fleet-wide road condition mapping that continuously updates a shared road health database.  

[1]      S. Nienaber, M. J. Booysen, and R. S. Kroon, "Detecting potholes using simple image processing techniques and real-world footage," in Proc. South African Transport Conf. (SATC), Pretoria, South Africa, Jul. 2015, pp. 1–10. [2]     A. Eriksson, M. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan, "The pothole patrol: Using a mobile sensor network for road surface monitoring," in Proc. 6th ACM Int. Conf. Mobile Syst. Appl. Serv. (MobiSys), Breckenridge, CO, USA, Jun. 2008, pp. 29–39.       [3]       A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, Apr. 2020.   [4]      J. Zou, H. Ye, and C. Ren, "Road surface defect detection using image processing techniques," IEEE Trans. Intell. Transp. Syst., vol. 18, no. 3, pp. 754–762, Mar. 2017.   [5]      Y. Li, H. Liu, X. Hu, L. Zhao, and H. Wan, "Pavement defect detection with fully convolutional network and uncertainty estimation," ISPRS Int. J. Geo-Inf., vol. 9, no. 1, p. 48, Jan. 2020.   [6]      H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, "Road damage detection and classification using deep neural networks with smartphone images," Comput.-Aided Civ. Infrastruct. Eng., vol. 33, no. 12, pp. 1127–1141, Dec. 2018.   [7]      W. Zhang, Z. Zhang, and X. Qi, "Automatic pavement defect detection using 3D laser profiling technology," Autom. Constr., vol. 96, pp. 350–365, Dec. 2018.   [8]      R. Fan, U. Ozgunalp, B. Hosking, M. Liu, and I. Pitas, "Pothole detection based on disparity transformation and road surface modeling," IEEE Trans. Image Process., vol. 29, pp. 897–908, 2020.   [9]      D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal, and Y. Sekimoto, "Deep learning-based road damage detection and classification for multiple countries," Autom. Constr., vol. 132, p. 103935, Dec. 2021.   [10]    K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778.