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Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js

Nidhi Ruhil1 , Devansh Sahni2 , Anushaka 3 , Anurag Wadhwa4 , Anjali Sharma5

  1. Dept. of Computer Science and Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies (Previously ADGITM), New Delhi, India.
  2. Dept. of Computer Science and Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies (Previously ADGITM), New Delhi, India.
  3. Dept. of Computer Science and Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies (Previously ADGITM), New Delhi, India.
  4. Dept. of Computer Science and Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies (Previously ADGITM), New Delhi, India.
  5. Dept. of Computer Science and Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies (Previously ADGITM), New Delhi, India.

Section:Research Paper, Product Type: Journal Paper
Volume-11 , Issue-12 , Page no. 26-31, Dec-2023

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v11i12.2631

Online published on Dec 31, 2023

Copyright © Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma, “Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.26-31, 2023.

MLA Style Citation: Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma "Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js." International Journal of Computer Sciences and Engineering 11.12 (2023): 26-31.

APA Style Citation: Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma, (2023). Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js. International Journal of Computer Sciences and Engineering, 11(12), 26-31.

BibTex Style Citation:
@article{Ruhil_2023,
author = {Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma},
title = {Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2023},
volume = {11},
Issue = {12},
month = {12},
year = {2023},
issn = {2347-2693},
pages = {26-31},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5644},
doi = {https://doi.org/10.26438/ijcse/v11i12.2631}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v11i12.2631}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5644
TI - Pothole Detection and Reporting System Implementation Using Yolov8 and TensorFlow.js
T2 - International Journal of Computer Sciences and Engineering
AU - Nidhi Ruhil, Devansh Sahni, Anushaka, Anurag Wadhwa, Anjali Sharma
PY - 2023
DA - 2023/12/31
PB - IJCSE, Indore, INDIA
SP - 26-31
IS - 12
VL - 11
SN - 2347-2693
ER -

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Abstract

Potholes present a substantial hazard to both road safety and the structural integrity of vehicles. This paper introduces a novel approach to pothole detection leveraging YOLOv8, an object detection algorithm, and TensorFlow.js. The proposed system aims to detect potholes accurately and swiftly by analysing live video feeds. The trained model exhibits promising performance metrics in pothole detection, with the bounding box precision at 0.822 and the mean Average Precision (mAP) value of 0.847, highlighting the model`s robustness in accurately localizing potholes. The proposed pothole detection system presents a promising solution for proactive road maintenance and safety enhancement. Its efficiency in real-time detection, combined with the adaptability of TensorFlow.js, holds the potential for widespread implementation, contributing to mitigating road hazards and infrastructure maintenance. The use of Tensorflow.js allows JavaScript developers to work with YOLOv8 reducing the dependency on Python for this purpose. The Pothole Detection and Reporting System with YOLOv8 and Tensorflow.js provides quite promising results.

Key-Words / Index Term

Object detection, YOLOv8, TensorFlow.js, Road safety, Pothole detection.

References

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