Analyzing Road Images for Pothole Detection through Machine Learning Algorithms: A Comprehensive Review
Downloads
The road accident prediction system leverages deep learning techniques, specifically YOLO (You Only Look Once), for the detection of potholes in road footage. By employing YOLO, the system can efficiently identify and localize potholes within video frames, enabling rapid and accurate detection. Furthermore, the system incorporates a severity prediction module that utilizes the dimensions and characteristics of detected potholes to assess their severity levels. This predictive capability empowers authorities and road maintenance teams to prioritize repair efforts and allocate resources effectively, ultimately contributing to the reduction of road accidents and ensuring safer road conditions for motorists and pedestrians alike. Through the seamless integration of pothole detection and severity prediction functionalities, the road accident prediction system offers a proactive approach to road maintenance and safety management, enhancing overall road infrastructure resilience and public safety.
Amita Dhiman and Reinhard Klette, “Pothole detection using Computer vision and learning”.
(2018).ChristchurchReport.[Online].Available:www.stuff.co.nz/the press/news/100847641/christchurch -the-pothole-capital-of-new-zealand/
Q. Li, M. Yao, X. Yao, and B. Xu, “A real-time 3D scanning system for pavement distortion inspection,” Meas. Sci. Technol., vol. 21, no. 8,pp. 015702-1–015702-8, 2010.
H. Maeda, Y. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road damage detection using deep neural networks with images captured through a smartphone,” 2018, arXiv:1801.09454. [Online]. Available: https://arxiv.org/abs/1801.09454
J. Ren and D. Liu, “PADS: A reliable pothole detection system using machine learning,” in Proc. Int. Conf. Smart Comput. Commun.,Jan. 2016, pp. 327–338.
Y.-W. Hsu, J. W. Perng, and Z.-H. Wu, “Design and implementation of an intelligent road detection system with multisensor integration,” in Proc. Int. Conf. Mach. Learn. Cybern., 2016, pp. 219–225
F. Seraj, B. J. van der Zwaag, A. Dilo, T. Luarasi, and P. Havinga,“RoADS: A road pavement monitoring system for anomaly detection using smart phones,” in Proc. Int. Workshop Modeling Social Media,Jan. 2014, pp. 128–146.
Y. Bhatia, R. Rai, V. Gupta, N. Aggarwal, and A. Akula, “Con volutional neural networks based potholes detection using thermal imaging,” King Saud Univ., Comput. Inf. Sci., to be published. doi: 10.1016/j.jksuci.2019.02.004
M. V. Thekkethala and S. Reshma, “Pothole detection and volume estimation, using stereoscopic cameras,” in Proc. Int. Conf. Mixed Design Integr. Circuits Syst., 2016, pp. 47–51.
S.-K. Ryu, T. Kim, and Y.-R. Kim, “Image-based pothole detection system for ITS service and road management system,” Math. Problems Eng., vol. 2015, 2015, Art. no. 968361.
L. Powell and K. G. Satheeshkumar, “Automated road distress detec tion,” in Proc. Int. Conf. Emerg. Technol. Trends, 2016, pp. 1–6. A. Rasheed, K. Kamal, T. Zafar, S. Mathavan, and M. Rahman, “Stabilization of 3D pavement images for pothole metrology using the Kalman filter,” in Proc. Int. Conf. Intell. Transp. Syst., 2015, pp. 2671–2676.
S. Nienaber, M. J. Booysen, and R. S. Kroon, “Detecting potholes using simple image processing techniques and real-world footage,” in Proc. Southern Afr. Transp. Conf., Jul. 2015.
Z. Ying, G. Li, X. Zang, R. Wang, and W. Wang, “A novel shadow-free feature extractor for real-time road detection,” in Proc. Int. Conf. Pervas. Ubiquitous Comput., 2016, pp. 611–615.
J. M. Alvarez, T. Gevers, and A. M. Lopez. 3d scene ´priors for road detection. In Computer Vision andPattern Recognition (CVPR), 2010 IEEE Conference on, pages 57–64. IEEE, 2010.
