Authors :
Nikita Gosavi; Dhawal Hirani; Ankita Budhwant; Rahul Patil; Harshal Joshi
Volume/Issue :
Volume 10 - 2025, Issue 2 - February
Google Scholar :
https://tinyurl.com/2m27fx2t
Scribd :
https://tinyurl.com/3hc2t7ns
DOI :
https://doi.org/10.5281/zenodo.14964338
Abstract :
Motorcycles have traditionally been one of the most widely used modes of transportation in developing
countries. However, in recent times, the number of motorcycle accidents has increased. One of the major contributing
factors to these accidents is the absence of helmets worn by riders. Traffic authorities monitor road intersections, review
CCTV footage, and take action against motorcyclists who fail to comply with helmet regulations. Enforcing this rule
typically requires human intervention, making it a labor-intensive process. To address this issue, this project proposes an
automated system that detects and extracts motorcycle number plates from riders without helmets using CCTV footage.
The extracted number plate is then processed using an Optical Character Recognition (OCR) algorithm to identify the
vehicle’s registration details. This AI- powered approach helps in identifying violators and notifying them to pay their
challans. The proposed system first captures real-time traffic images before distinguishing motorcycles from other vehicles
on the road.
Keywords :
Helmet Detection, Number Plate Detection, Data Manipulation and Transfer, Data Storage and Server, Encryption and Web Connection.
References :
- Priyanshi tripathia, pragati singha ,mantsha banoa ,komal sharmaa, abhishek shahib abachelor of technology student, department of infor- mation technology,buddha institute of technology, gorakhpur, pin code 273209,india assistant professor, department of computerscience engi- neering, buddha institute of technology, gorakhpur, pin code 273209,in- dia.
- Abhijeet singh, dalveer singh, jasjeet singh, prem singh, dr. amdeep kaur. guru tegh bahadurinstitute of technology, new delhi, india.
- y.priyatham kumar1 , s.bharath2 , p.srikar sai3 , dharmavaram asha devi4 1,2,3 ug researchscholar, department of electronics and communication engineering, srinidhi institute of science and technology, hyderabad, telangana, india.
- Mohit gupta, naman tyagi, ritika mittal, princy, mr. shahid department of computer science and engineering meerut institute of engineering and technology, meerut, up, india.
- M. Vaidarbhi A. D. Dharmavaram, ”automatic vehicle identi- fication and recognition usingcnn implemented on pynq board,” 2022 6th international conference on electronics, communication and aerospace technology,coimbatore, india, 2022,pp. 1302-1306, doi: 10.1109/iceca55336.2022.10009054.
- P. Doungmala K. Klubsuwan, ”helmet wearing detection in thailand using haarlike featureand circle hough transform on image processing,” 2016 ieee international conference on computer and information tech- nology (cit), nadi, 2016, pp. 611-614.
- K. Dahiya, D. Singh C. K. Mohan, ”automatic detection of bike-riders without helmet using surveillance videos in real-time,” 2016 interna- tional joint conference on neural networks (ijcnn)detection of helmets on motorcyclists. by remuera r.v.e silva, kelson r.t. aires, rodrigode m.s. veras.
- Remuera R. V. E. Silva, Kelson R. T. Aires, Rodrigo de M. S. Veras, et al., ”Detection of helmets on motorcyclists using convolutional neural networks,” 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 1169-1174.
- N. Kumar, P. Yadav, and A. Pandey, ”YOLO-based automated detection of helmet and license plate for traffic monitoring systems,” Journal of Intelligent Transportation Systems, vol. 15, no. 2, pp. 185-198, 2023.
- T. H. Vu, H. Nguyen, and Q. Tran, ”Integration of YOLOv5 and OCR for real-time e-challan systems,” International Journal for Multidisciplinary Research (IJFMR), vol. 6, no. 2, March 2024.
- P. Singhal, V. Malhotra, and S. Bhardwaj, ”Real-time number plate detection using YOLOv5 and EasyOCR,” 2023 IEEE Symposium on Emerging Technologies (SET), New York, NY, USA, 2023, pp. 450- 455.
- Jin et al., ”YOLO-ESCA: A High-Performance Safety Helmet Detection Algorithm,” IEEE Xplore, 2023, introduces an enhanced YOLOv5 model with improved accuracy for detecting helmets in various conditions
- Navita Ms Sonia Batra, ”Helmet detection and challan generation using YOLO and OCR,” International Research Journal of Modernization in Engineering Technology and Science, 2024, focuses on integrating YOLO for detection and OCR for license plate recognition in real-time
- ”Real-Time Helmet Violation Detection in AI City Challenge 2023,” arXiv, describes the application of YOLOv5 for detecting helmet viola- tions and integrating ensemble learning techniques
- Verma and S. Gupta, ”Automated Traffic Rule Enforcement System Using Deep Learning,” International Journal of Computer Applications, which reviews systems that integrate license plate and safety gear detection for law enforcement.
- R. V. E. Silva, K. R. T. Aires, and R. M. S. Veras: Detection of Helmets on Motorcyclists. This research examines helmet detection methodologies, providing insights into CNN-based approaches for rider safety monitoring.
- Liu et al. (2020): Helmet and License Plate Detection Using YOLOv3 Algorithm. This study achieved 97.4% accuracy for number plate detection and 98.3% for helmet detection in real-time traffic surveillance applications
- Li et al. (2019): Deep Learning-Based Helmet Detection Using Faster R-CNN. Achieved an accuracy of 95.6% on custom datasets
- Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR: A 2024 study discussing YOLOv5 for object detection and EasyOCR for plate recognition, emphasizing real- time applications
- Sudarsan et al.: Integrated Helmet Detection and License Plate Recog- nition for Traffic Monitoring. Focused on using YOLOv5 for efficient detection and OCR for character recognition
- Chen et al., ”Real-time Helmet Detection using YOLOv5,” 2024 IJARIIE. The authors propose a system combining YOLOv5 with online hard example mining for efficient real-time detection
- Patel et al., ”Enhanced Helmet Detection for Bike Riders,” 2024 IJARIIE. This paper explores YOLOv5 integrated with attention mech- anisms and ensemble models to improve detection accuracy
- Wang and Gupta, ”A Comparative Study of Helmet Detection Models,” 2023 IJARIIE. This study evaluates YOLOv5 against YOLOv4 and Faster R-CNN for helmet detection on VOC datasets
- IEEE Xplore, ”Enhancing Road Safety with Real-Time Helmet De- tection and E-Challan Issuance using YOLO and OCR,” 2024 IEEE Conference Publication. This work integrates YOLO for object detection and OCR for number plate recognition to streamline e-challan issuance.
- IJRPR, ”Advanced Traffic Monitoring and Enforcement using YOLOv8,” 2024 International Journal of Research Publication and Re- views. This paper discusses a multi-module system for helmet detection and license plate recognition leveraging YOLOv8
- J. Li and S. Wang, ”Helmet Net: Improved YOLOv8 Algorithm for Helmet Wearing Detection,” Springer Journal on Advanced Neural Networks, 2023. This paper enhances YOLOv8 for better detection accuracy under challenging conditions like occlusion and low light
- M. Kim et al., ”Real-Time Helmet Violation Detection with AI Sys- tems,” AI City Challenge Proceedings, 2023. Highlights deep learning frameworks for ensuring motorcycle safety compliance
- T. Nguyen et al., ”Enhanced Traffic Violation Enforcement System Us- ing YOLO and OCR,” IEEE Transactions on Intelligent Transportation Systems, 2024. Proposes a scalable system integrating YOLO and OCR for real-time monitoring
- Sharma, ”Revolutionizing Road Safety with AI-Powered Helmet Detection,” ER Publications, 2024. Combines YOLO’s object detection with advanced OCR systems for real-time e-challan generation
- Hari Krishnan, R., Enhancing Road Safety with Real-Time Helmet Detection and Challan Issuance Using YOLO and OCR. This research discusses a real-time surveillance system that identifies helmet-less riders using YOLO and OCR for number plate recognition to generate e- challans automatically. (International Research Journal of Modernization in Engineering, Technology, and Science, 2024)
- Manisha Pise et al., Helmet and Number Plate Detection Using YOLOv5. This research highlights a robust system integrating YOLOv5 for object detection and OCR for text recognition, achieving improved detection rates in real-world environments. (IJARSCT, 2024)
- YOLOv5-Based System (2024). ”Real-Time Automatic Number Plate and Helmet Recognition System.” Published in IEEE Xplore, this paper discusses a system integrating YOLOv5 for robust real-time detection and OCR technologies
- Majumder, J. (2022). ”Real-time YOLO-based monitoring for helmet and number plate detection.” This research emphasizes the integration of YOLO for helmet identification and OCR for number plate recognition in automated traffic violation systems
Motorcycles have traditionally been one of the most widely used modes of transportation in developing
countries. However, in recent times, the number of motorcycle accidents has increased. One of the major contributing
factors to these accidents is the absence of helmets worn by riders. Traffic authorities monitor road intersections, review
CCTV footage, and take action against motorcyclists who fail to comply with helmet regulations. Enforcing this rule
typically requires human intervention, making it a labor-intensive process. To address this issue, this project proposes an
automated system that detects and extracts motorcycle number plates from riders without helmets using CCTV footage.
The extracted number plate is then processed using an Optical Character Recognition (OCR) algorithm to identify the
vehicle’s registration details. This AI- powered approach helps in identifying violators and notifying them to pay their
challans. The proposed system first captures real-time traffic images before distinguishing motorcycles from other vehicles
on the road.
Keywords :
Helmet Detection, Number Plate Detection, Data Manipulation and Transfer, Data Storage and Server, Encryption and Web Connection.