Open Access
Issue |
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
|
|
---|---|---|
Article Number | 01008 | |
Number of page(s) | 12 | |
Section | Artificial Intelligence | |
DOI | https://doi.org/10.1051/itmconf/20235301008 | |
Published online | 01 June 2023 |
- Madhumathy P, Saurabh Singh, Shivamshukla, Unnikrishnan, Dayanandasagar., 2017. Detection of humps and potholes on roads and notifying the same to the drivers, International Journal of Management and Applied Science, Volume-3, pp294326 . [Google Scholar]
- P. Gurusamy, M. Anusha, N. Devipriya, P. Harini, MD Asrar Ul Haque., 2020. Speed Control of Vehicle by Detection of Potholes and Humps. International Journal of Broadband Cellular Communication Vol 6, p2. [Google Scholar]
- Prof. R.M. Sahu, Mayank Kher, Nurul Hasan, Laxmi Pancha., 2018. Automatic Detection of Potholes and Humps on Roads to Aid Driver. International Journal of Engineering Science and Innovative Technology Volume 7, p334-338 . [Google Scholar]
- K. Mohanprakash, S. Brindha, S. Gowri, K. Latha Maheswari., 2023. Implementation of vehicle speed limiting and pothole identification using ultrasonic sensor. International Journal of Management and Applied Science. Volume-9, Issue-1. [Google Scholar]
- R. Sundar, S. Hebbar, and V. Golla., 2017 Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection, International Journal of Recent Contributions from Engineering Science & IT IEEE vol. 15, no. 2, pp. 1109–1113. [Google Scholar]
- J. Lin and Y. Liu ., 2010 Potholes detection based on SVM in the pavement distress image, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering in Proc. 9th Int. Symp. Diatribe. Computer. Appl. Bus. Eng. Sci., pp. 544–547. [Google Scholar]
- S. S. Rode, S. Vijay, P. Goyal, P. Kulkarni, and K. Arya., 2010., Pothole detection and warning system: Infrastructure support and system design, International Journal of Computer Trends and Technology in Proc. Int. Conf. Electron. Computer. Technol., pp. 286–290. [Google Scholar]
- Kshitij Pawar, Siddhi Jagtap and Smita Bhoir., 2020., Efficient pothole detection using smartphone sensors. ITM Web of Conferences 32, pp 118-128. [Google Scholar]
- Hyunwoo Song, Kihoon Baek and Yungcheol Byun., 2020., Pothole Detection using Machine Learning, Conference: Advanced Science and Technology, pp 544–547. [Google Scholar]
- C. Koch and I. Brilakis, 2017., Improving Pothole Recognition through Vision Tracking for Automated Pavement Accessment, International Workshop on Intelligent Computing in Engineering, pp 603-617. [Google Scholar]
- M. I. Rajab, M. H. Alawi, M. A. Saif., 2019 Application of Image Processing to Measure Road Distresses, WSEAS Transactions on Information Science& Application, Automating Highway Infrastructure Maintenance Using Unmanned Aerial Vehicles Issue 1, Volume 5, pp 118129. [Google Scholar]
- X. Yao, M. Yao and B. Xu., 2021 Automated Detection and Identification of Areabased Distress in Concrete Pavements, 7th International Conference on Managing Pavement Assets, pp 12839–12855. [Google Scholar]
- E. Salari and E. Chou., 2011 Pavement Distress Evaluation Using 3D Depth Information from Stereo Vision, The University of Toledo Research and Sponsored Programs, p3845. [Google Scholar]
- L. Cruz, D. Lucio, L. Velho., 2012 Kinect and RGBD Images: Challenges and Applications, Conference on Graphics, Patterns and Images, pp320-328. [Google Scholar]
- Samyak Kathane, Vaibhav Kambli, Tanil Patel and Rohan Kapadia., 2015 Real Time Potholes Detection and Vehicle Accident Detection and Reporting System and Antitheft (Wireless), International Journal of Computer Trends and Technology, Vol. 21 [Google Scholar]
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