Smart Pothole Detection and Road Monitoring Systems: A Review of Sensing Methods, Deep-Learning Detectors and an Implemented Repair-Tracking Prototype

1. Y. Safyari, M. Mahdianpari, and H. Shiri, “A review of vision-based pothole detection methods using computer vision and machine learning,” Sensors, vol. 24, no. 17, Art. no. 5652, 2024, doi: 10.3390/s24175652.
2. Mohan Prakash B and K. C. Sriharipriya, “Enhanced pothole detection system using YOLOX algorithm,” Autonomous Intelligent Systems, vol. 2, no. 1, Art. no. 22, 2022, doi: 10.1007/s43684-022-00037-z.
3. C. Wu, Z. Wang, S. Hu, J. Lepine, X. Na, D. Ainalis, and M. Stettler, “An automated machine-learning approach for road pothole detection using smartphone sensor data,” Sensors, vol. 20, no. 19, Art. no. 5564, 2020, doi: 10.3390/s20195564.
4. M. Zareei, C. A. López Castañeda, F. Alanazi, F. Granda, and J. A. Pérez-Díaz, “Machine learning model for road anomaly detection using smartphone accelerometer data,” IEEE Access, vol. 13, pp. 122841–122851, 2025.
5. T. Tamagusko and A. Ferreira, “Optimizing pothole detection in pavements: A comparative analysis of deep learning models,” Eng. Proc., vol. 36, no. 1, p. 11, 2023, doi: 10.3390/engproc2023036011.
6. I. A. Febriansyah, E. Darmanto, and P. Setiaji, “Benchmarking YOLOv8n, YOLOv11n, and YOLOv12n for smart road pothole detection,” Edumatic: Jurnal Pendidikan Informatika, vol. 10, no. 2, pp. 400–409, 2026, doi: 10.29408/edumatic.v10i2.35908.
7. N. Ma, J. Fan, W. Wang, J. Wu, Y. Jiang, L. Xie, and R. Fan, “Computer vision for road imaging and pothole detection: A state-of-the-art review of systems and algorithms,” Transportation Safety and Environment, vol. 4, no. 4, 2022, doi: 10.1093/tse/tdac026.8. Z. S. Mohd Shah and M. N. Haji Mohd, “Real-time pothole detection using deep learning,” Evol. Elect. Electron. Eng., vol. 5, no. 1, pp. 459–469, 2024.
9. J. Dib, K. Sirlantzis, and G. Howells, “Application of deep learning techniques in negative road anomalies detection,” in Proc. 14th Int. Joint Conf. Comput. Intell. (IJCCI), ROBOVIS, 2022, pp. 475–482, doi: 10.5220/0011336000003332.
10. M. H. Asad, S. Khaliq, M. H. Yousaf, M. O. Ullah, and A. Ahmad, “Pothole detection using deep learning: A real-time and AI-on-the-edge perspective,” Advances in Civil Engineering, vol. 2022, Art. no. 9221211, 2022, doi: 10.1155/2022/9221211.
11. M. Jakubec, E. Lieskovska, B. Bučko, and K. Zábovská, “Comparison of CNN-based models for pothole detection in real-world adverse conditions: Overview and evaluation,” Appl. Sci., vol. 13, no. 9, Art. no. 5810, 2023, doi: 10.3390/app13095810.
12. J. Frnda, S. Bandyopadhyay, M. Pavlicko, M. Durica, M. Savrasovs, and S. Banerjee, “Analysis of pothole detection accuracy of selected object detection models under adverse conditions,” Transport and Telecommunication, vol. 25, no. 2, pp. 209–217, 2024, doi: 10.2478/ttj-2024-0016.
13. Y. Zanevych, V. Yovbak, O. Basystiuk, N. Shakhovska, S. Fedushko, and S. Argyroudis, “Evaluation of pothole detection performance using deep learning models under low-light conditions,” Sustainability, vol. 16, no. 24, Art. no. 10964, 2024, doi: 10.3390/su162410964.
14. M. Yurdakul and Ş. Taşdemir, “An enhanced YOLOv8 model for real-time and accurate pothole detection and measurement,” arXiv:2505.04207, 2025.
15. C. F. Ahmad et al., “Classification of potholes based on surface area using pre-trained models of convolutional neural network,” arXiv:2309.17426, 2023.
16. D. Arya, H. Maeda, S. K. Ghosh, D. Toshniwal, and Y. Sekimoto, “RDD2022: A multi-national image dataset for automatic road damage detection,” Geoscience Data Journal, vol. 11, 2024, doi: 10.1002/gdj3.260.
17. P. Singh, B. Likhitha, K. Sahithi, D. G. Reddy, D. Keerthana, and K. P. Adarsh, “Smart road maintenance: Real-time surface damage detection and mapping with YOLOv8,” Int. J. Informatics Commun. Technol., vol. 15, no. 3, pp. 1331–1339, 2026, doi: 10.11591/ijict.v15i3.pp1331-1339.