M.S. Kamalesh1 , Bharatiraja Chokkalingam2 , Jeevanantham Arumugam3 , Gomathy Sengottaiyan1 , Shanmugavadivel Subramani1 , and Mansoor Ahmad Shah2

1Department of Electrical and Electronics Engineering, Kongu Engineering College, Tamilnadu, India.
2Department of Electrical and Electronics Engineering, SRM Institute of Science Technology, Tamilnadu, India
3Department of Information Technology, Kongu Engineering College, Tamilnadu, India.


Received: June 27, 2020
Accepted: August 25, 2020
Publication Date: February 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202102_24(1).0010  


One of the major causes for road accidents in Indian roads is due to the potholes and humps. There is currently no system for detecting and identifying such potholes; thus, human intervention checks are performed manually, resulting in poor road maintenance, particularly in villages. In this digital era, it becomes essential to identify and report these potholes to the corresponding authorities in an automated version. This paper proposes a simple IoT based low-cost portable and economically affordable device to detect the potholes and intimate the damaged scenario to the corresponding authorities. This methodology avoids road accidents and ensures the proper maintenance. This proposed system can be installed on the moving vehicle and images are captured and mailed to the respective authority along with the GPS coordinates, which further alert the authorities to take corrective measures. This paper mainly focuses on implementing the above technology to motorbikes in a customized version as per the user requirements. The coordinates of the Global Positioning System (GPS) location will be stored in the ThingsBoard server and hosted in the Amazon Web Service. The entire system was successfully implemented using Raspberry Pi3 Single Board Computer (SBC) to capture, analyze the images and for email communication protocol. Amazon Web Service (AWS) is used as a backend to store the GPS location of the damaged rods with 100 % reporting success rate.

Keywords: Raspberry Pi3, Amazon Web Service, Things Board, Pothole detection, IoT system


  1. [1] Jinesh Mehta, Vinayak Mathur, Dhruv Agarwal, Atish Sharma, and Krishna Prakasha. Pothole detection and analysis system (PODAS) for real time data using sensor networks. Journal of Engineering and Applied Sciences, 12(12):3090–3097, 2017.
  2. [2] Lokeshwor Huidrom, Lalit Kumar Das, and S K Sud. Method for Automated Assessment of Potholes, Cracks and Patches from Road Surface Video Clips Method for automated assessment of potholes, cracks and patches from road surface video clips Selection and peer-review under responsibility of International Sci. Procedia - Social and Behavioral Sciences, 104:312–321, dec 2013.
  3. [3] Che-I Wu, Hsu-Yang Kung, Chi-Hua Chen, and LiChia Kuo. An intelligent slope disaster prediction and monitoring system based on WSN and ANP. Expert Systems with Applications, 41(10):4554–4562, aug 2014.
  4. [4] C Koch, I Brilakis Advanced Engineering Informatics, and Undefined 2011. Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25(3):507–515, 2011.
  5. [5] DI Dikii 2020 IEEE Conference of Russian Young and Undefined 2020. Remote Access Control Model for MQTT Protocol. Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus, (2020):288–291, 2020.
  6. [6] Qingguang Li, Ming Yao, Xun Yao, and Bugao Xu. A real-time 3D scanning system for pavement distortion inspection. Measurement Science and Technology, 21(1), 2010.
  7. [7] Kamalesh M, Vikashini M and Pradeep S. Precompensated Master Slave Control of Parallel DC-DC Converter in DC-Microgrid. Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018, 2018.
  8. [8] M Ramalingam, A Jeevanandham, P Keerthana, D Indumathi, M Nithyashree, S ThilakRaj, and N Naveenkumar. Smart Attendance Monitoring System to Avoid Fraudulence by Synchronizing Results of RFID and Face Recognition System. 2017.
  9. [9] X. Yu and E. Salari. Pavement pothole detection and severity measurement using laser imaging. In 2011 IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY, pages 1–5. IEEE, may 2011.
  10. [10] T Kim, SK Ryu. Review and analysis of pothole detection methods. Journal of Emerging Trends in Computing and Information Sciences, 5(8):603– 608, 2014.
  11. [11] C. Bharatiraja, Sanjeevikumar Padmanaban, Pierluigi Siano, Ramesh Krishnamoorthy, and Raghu Selvaraj. Real-time forecasting of EV charging station scheduling for smart energy systems. Energies, 10(3), mar 2017.
  12. [12] K Ramesh, C Bharatiraja, S Raghu. Design and implementation of real time charging optimization for hybrid electric vehicles. International Journal of Power Electronics and Drive Systems, 7(4):1261–1268, 2016.
  13. [13] M S Kamalesh, Nattuthurai Senthilnathan, and Chokkalingam Bharatiraja. Design of a Novel Boomerang Trajectory for Sliding Mode Controller. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2020.

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