Smart Pothole Detection and Mapping System
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How to Cite

Borgalli, Rohan. 2020. “Smart Pothole Detection and Mapping System”. Journal of Ubiquitous Computing and Communication Technologies 2 (3): 136-44. https://doi.org/10.36548/jucct.2020.3.003.

Keywords

— pothole
— detection
— mapping
— Internet of Things(IoT)
— image processing
— deep learning
Published: 08-09-2020

Abstract

In any country public services and infrastructure is very crucial part of development. The qualities of these are shed light on how well the government is doing its job, the consequences of disparities in local funding. But there are few answers on these many questions. This is why this system was implemented to detect all types of potholes using different methods based on data acquired by ultrasonic sensor, gyroscope and image acquired by Pi-camera which gives the intensity and shape of the pothole in any given condition. Potholes have been a major problem in Mumbai road in recent times. Proposes system is trying to study potholes and their distribution on the roads of Mumbai with the help of hardware mentioned to acquire images and deep learning framework for potholes detection and mapping then using Android App and Google map it is generalize to the entire city.

References

  1. HOFFMANN, D. “Statistical size analysis of potholes: an attempt to estimate geological losses ahead of mining at Lonmin’s Marikana mining district.” The 4th International Platinum Conference, Platinum in transition ‘Boom or Bust’, The Southern African Institute of Mining and Metallurgy, 2010.
  2. Kim, Taehyeong, and Seung-Ki Ryu. "Review and analysis of pothole detection methods." Journal of Emerging Trends in Computing and Information Sciences 5, no. 8 (2014): 603-608.
  3. Laohaprapanon, Suriyan, Kimberly Ortleb, and GauravSood. "Street Sense: Learning from Google Street View." arXiv preprint arXiv: 1807.06075 (2018).
  4. Patra, Suvam, Pranjal Maheshwari, Shashank Yadav, Subhashis Banerjee, and ChetanArora. "A joint 3d-2d based method for free space detection on roads." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 643-652.IEEE, 2018.
  5. Wang, Huaijun, Na Huo, Junhuai Li, Kan Wang, and Zhixiao Wang. "A Road Quality Detection Method Based on the Mahalanobis-Taguchi System." IEEE Access 6 (2018): 29078-29087.
  6. El-Wakeel, Amr S., AboelmagdNoureldin, Hossam S. Hassanein, and Nizar Zorba. "iDriveSense: Dynamic Route Planning Involving Roads Quality Information." In 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1-6.IEEE, 2018.
  7. Pan, Yifan, Xianfeng Zhang, Guido Cervone, and Liping Yang. "Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 10 (2018): 3701-3712.
  8. F. Kalim, J. P. Jeong and M. U. Ilyas, "CRATER: A Crowd Sensing Application to Estimate Road Conditions," in IEEE Access, vol. 4, pp. 8317-8326, 2016.
  9. M. M. Garcillanosa, J. M. L. Pacheco, R. E. Reyes and J. J. P. San Juan, "Smart Detection and Reporting of Potholes via Image-Processing using Raspberry-Pi Microcontroller," 2018 10th International Conference on Knowledge and Smart Technology (KST), Chiang Mai, 2018, pp. 191-195.
  10. Chakravorty, Pragnan (2018). "What is a Signal?[Lecture Notes]". IEEE Signal Processing Magazine. 35 (5): 175–177.