Enforce360: YOLOv8 and Tesseract-OCR for Comprehensive Helmet Rule Adherence


EOI: 10.11242/viva-tech.01.05.001

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Citation

Archana Ingle, Manali Kadam, Sourav Samanta, Ashish Thakur, "Enforce360: YOLOv8 and Tesseract-OCR for Comprehensive Helmet Rule Adherence", VIVA-IJRI Volume 1, Issue 7, Article 1, pp. 1-9, 2024. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

“Enforce360: YOLOv8 and Tesseract-OCR for Comprehensive Helmet Rule Adherence” Motorcycle riders not wearing helmets pose a significant risk to road safety and their own wellbeing. However, manually monitoring a large volume of traffic across an entire city to identify all helmet violations is an impossible task. Conversely, automatically extracting license plate information from vehicles promises various applications like seamless toll payments, parking systems, etc. but relies heavily on expensive infrastructure changes. This paper tackles these challenges by developing a real-time computer vision system to automatically detect helmet violations among motorcycle riders while concurrently extracting their license plate details. By employing advanced machine learning algorithms for object detection and optical character recognition, the system can accurately identify riders without helmets and capture license plate numbers with high precision. The real-time nature allows it to monitor live traffic streams and generate actionable insights for law enforcement and other agencies. The proposed techniques provide an efficient and low-cost automated framework to improve road safety by increasing compliance to helmet rules. Additionally, the license plate data extraction opens up possibilities for various smart city applications without needing large investments into infrastructure changes. The system has the potential to process large volumes of traffic across cities and contribute significantly to road safety and intelligent transportation initiatives.

Keywords

Object Detection, YOLOV8, Optical Character Recognition (OCR), Deep Learning

References

  1. Automatic Number Plate Recognition System (ANPR): A Survey, By:- Chirag Patel , Dipti Shah, G. H. Patel, Atul Patel- International Journal of Computer Applications (0975 – 8887) Volume 69– No.9, May 2013.
  2. Surveillance system for monitoring bike riders without Helmet and triple riders using MATLAB, By:- K.V.Lalitha, N. Ragha Sri, Mohan Krishna, K. Nithin Aaron, K. Sumanth Babu
  3. Car Plate Recognition System,By:- Dening Jiang,Tulu Muluneh Mekonnen,Tiruneh Embiale Merkebu
  4. Detection of Motorcyclists without Helmet in Videos using Convolutional Neural Network, By: - C. Vishnu, Dinesh Singh, C. Krishna Mohan and Sobhan Babu Visual Intelligence and Learning Group (VIGIL), Department of Computer Science and Engineering Indian Institute of Technology Hyderabad, Kandi
  5. Machine Learning based Surveillance System for Detection of Bike Riders without Helmet and Triple Rides,By: Apoorva Saumya, Gayathri.V, Dr. Venkateswaran K, Sarthak Kale, Dr.Sridhar N Department of Electronics and Communication Engineering
  6. Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method,By: Prajwal M. J., Tejas K. B., Varshad V., Mahesh Madivalappa Murgod, Shashidhar R. - International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-2, December 2019.
  7. TRAFFIC VIOLATION PROCTORING SYSTEM: HELMET AND TRIPLE RIDING DETECTION, By: M.Jayasree, P.Sai Vamsi Dheeraj , S.Sindhura, K.Lokesh – Published on IRJET, March – 2019.
  8. A Review Paper on Automatic Number Plate Recognition System using Machine Learning Algorithms, by: - Shraddha S. Ghadage, Mr. Sagar R. Khedkar, International Journal of Engineering Research & Technology (IJERT), 8 Issue 12, December-2019.
  9. Automatic Detection of Bike-riders without Helmet using Surveillance Videos in Real-time By: - Kunal Dahiya, Dinesh Singh, C. Krishna Mohan Visual Learning and Intelligence Group (VIGIL), Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India.
  10. A Study On Object Detection, By: - S.Manjula, Dr.K.Lakshmi,, Research Scholar, b Professor, Computer Science and Engineering, Periyar Maniammai University, Thanjavur-613403, Tamilnadu, India. , Issue,20 Oct 2016