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Helmet and Number Plate Detection
Subject area: Science,Engineering and Technology · Area of research: Deep Learning, AIML, Image and Video Processing
Abstract
This report presents a real-time object detection system for identifying motorcyclists without helmets and detecting vehicle number plates using the YOLO (You Only Look Once) algorithm. The primary objective is to enhance road safety and aid law enforcement by automating the surveillance process. The model is trained using a dataset consisting of annotated images of motorcyclists with and without helmets, as well as vehicles with visible number plates. YOLO?s fast and accurate detection capabilities enable efficient identification of both safety violations and vehicle registration details. The system successfully detects and classifies helmet usage and localizes number plates in real-time, making it suitable for deployment in smart traffic monitoring systems. The results demonstrate high precision and recall, confirming YOLO?s effectiveness in multi-object detection tasks within the traffic surveillance domain.
References
[1] Chidananda K. et al. Real-time object detection using yolo v5 deep learning model. Journal of Real-Time Object Detection, 2023.
[2] J. Reddy Pasam et al. Detection of helmets and license plates using deep learning techniques. Journal of Traffic Enforcement and Monitoring, 2022.
[3] B. Amoolya et al. Deep learning methods for helmet detection and license plate recognition. Journal of Deep Learning in Transportation Systems, 2021.
[4] A. R. et al. Survey of helmet detection and number plate recognition techniques. Journal of Machine Learning and Applications, 2021.
[5] Kulkarni P. S et al. Real-time helmet detection using deep learning and yolo. Journal of Deep Learning and Real-Time Applications, 2021.
[6] Ghazali R et al. Vehicle detection for smart city applications using yolov3. Journal of Smart City Technologies, 2021.
[7] Wu Y et al. Deep learning applications in intelligent transportation systems. Journal of Intelligent Transportation Systems, 2021.
[8] R. Roy et al. Machine learning techniques for helmet and license plate recognition. Journal of Machine Learning in Traffic Management, 2021.
[9] Ahmad S et al. Automatic license plate recognition using yolov3 and opencv. Journal of Automated Traffic Monitoring, 2021.
How to cite this paper
@article{1708702,
author = {Pawan Kumar Singh, Syed Md Sheeraz , Udit Singh, Madhup Agrawal},
title = {Helmet and Number Plate Detection},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {2085-2108},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708702.pdf},
abstract = {This report presents a real-time object detection system for identifying motorcyclists without helmets and detecting vehicle number plates using the YOLO (You Only Look Once) algorithm. The primary objective is to enhance road safety and aid law enforcement by automating the surveillance process. The model is trained using a dataset consisting of annotated images of motorcyclists with and without helmets, as well as vehicles with visible number plates. YOLO?s fast and accurate detection capabilities enable efficient identification of both safety violations and vehicle registration details. The system successfully detects and classifies helmet usage and localizes number plates in real-time, making it suitable for deployment in smart traffic monitoring systems. The results demonstrate high precision and recall, confirming YOLO?s effectiveness in multi-object detection tasks within the traffic surveillance domain.},
month = {May},
}