Detection of Helmet and License Plate Using Machine Learning

Authors

  • Gopinath D. Department of ECE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India
  • Deiva Nayagam R. Department of ECE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India
  • Arun Raj V. Department of ECE, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
  • Davidson Kamaladhas M. Department of ECE, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
  • Sanmuga Priya M. Department of Information Technology, Sethu Institute of Technology, Virudhunagar, Tamilnadu, India
  • Karthik S. Department of ECE, SSM Institute of Engineering and Technology, Dindigul, Tamilnadu, India

Keywords:

Convolutional Neural Network, Helmet Detection, Machine Learning, Yolov8

Abstract

Individuals frequently disregard how important it is to wear helmets, which causes tragic accidents.  A helmet reduces your risk of getting a serious brain injury and dying by deflecting most of the impact energy that would otherwise hit your head and brain during a tumble or collisions. In India, it is against the law to operate a motorbike or scooter without a helmet, which has increased fatalities as well as crashes.  The existing system mostly relies on surveillance footage for keeping up with traffic violations, necessitating a close-up of the license plate by traffic police in the case that the motorcyclist lacks a helmet. Yet, this necessitates a substantial amount of personnel and time considering the high frequency of traffic violations and the rising everyday use of motorcycles.  Imagine if there was an algorithm that monitored traffic infractions, such driving a motorbike with no a helmet, and, if any were identified, generate the license plate of the vehicle that committed the violation. Helmet and license plate is detected using a neural network is proposed in this paper. There will be two phases. Initially, we check to see if the riders are wearing helmets. If not, a second step is used to find their license plate.  To identify unauthorized vehicles, we also look for license plates on passing vehicles.

Downloads

Download data is not yet available.

References

Lin, H., Deng, J.D., Albers, D. and Siebert, F.W., Helmet use detection of tracked motorcycles using CNN-based multi-task learning, IEEE Access,2020; 162073-162084.

Sneha A. Ghonge, Jignyasa B. Sanghavi, Smart surveillance system for automatic detection of license plate number of motorcyclists without Helmet, International Journal of Computer Sciences and Engineering, Volume-6,2018.

Ashtari, A. H., Nordin, M. J., & Fathy, M. An Iranian license plate recognition system based on color features, IEEE transactions on intelligent transportation systems, 15(4),2014; 1690-1705.

Prajwal M. J., Tejas K. B., Varshad V., Mahesh Madivalappa Murgod, Shashidhar R, Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method, International Journal of Innovative Technology and Exploring Engineering(IJITEE) ISSN: 2278-3075, Volume-9 Issue-2, December 2019.

Lokesh Allamki., Manjunath Panchakshari., Ashish Sateesha., K S Pratheek. Helmet Detection using Machine Learning and Automatic License Plate Recognition, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 06, Issue: 12 | Dec 2019.

Thirunavukkarasu.M., Bugade Amoolya., Bulusu Vyagari Vaishnavi. Helmet detection and license plate recognition, International Journal of Computer Science and Mobile Computing, Vol.10(4), 2021.

Siddharth Singh, Padmini Mishra, Siddhartha Ojha, Mohd Shoaib, Helmet and license plate detection, International Research Journal of Modernization in Engineering Technology and Science, Volume:05/Issue:02/February-2023.

Feng, H. and Hu, J., Helmet wearing detection using improved single shot multibox detector. 2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) IEEE. 2022; 444-447.

Zhao, Baining, et al. Detection and location of safety protective wear in power substation operation using wear-enhanced YOLOv3 Algorithm, IEEE Access 9.2021; 125540-125549.

Hsu, Wei-Yen, and Wen-Yen Lin. "Ratio-and-scale-aware YOLO for pedestrian detection." IEEE transactions on image processing 30 2020; 934-947.

Bochkovskiy A, Wang CY, Liao HY. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. 2020.

Mohit Gupta, Naman Tyagi, Ritika Mittal, Princy, Mr. Shahid. Helmet and number plate detection using Yolov-3, Journal of Pharmaceutical Negative Results, Volume 13, Special Issue 10, 2022.

Chen S, Lan J, Liu H, Chen C, Wang X. Helmet wearing detection of motorcycle drivers using deep learning network with residual transformer-spatial attention. drones. 2022; 6(12):415.

Khan, M.A., Sharif, M., Javed, M.Y., Akram, T., Yasmin, M. and Saba, T., License number plate recognition system using entropy-based features selection approach with SVM. IET Image Processing, 12: 2018; 200-209.

Amit alhat, Dattatray Khandelwal, Automated helmet detection system, International Journal of Emerging Technologies and Innovative Research, Volume 9, Issue 8 2022; 317-328.

Shravani Maliye, Jayom Oza, Jayesh Rane, Nileema Pathak, Mask and helmet detection in Two-Wheelers using YOLOv3 and canny edge detection, International Research Journal of Engineering and Technology, Volume: 08, 2021.

Downloads

Published

24.03.2024

How to Cite

D., G. ., Nayagam R., D. ., Raj V., A. ., Kamaladhas M., D. ., Priya M., S. ., & S., K. . (2024). Detection of Helmet and License Plate Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 698–705. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5301

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.