Automatic Helmet and Number Plate Detection System
Asst. Prof Rewa Joshi1 , Shubham S. Sonone2 , Ajinkya S. Gaikwad3 , Tejas S. Jumle,4 Tejas D. Chaudhari5
1Ass. Prof. Department of Electrical Engineering TSSM'S BHIVARABAI SAWANT COLLEGE OF ENGINEERING AND RESEARCH
2345Students, Department of Electrical Engineering, TSSM'S BHIVARABAI SAWANT COLLEGE OF ENGINEERING AND RESEARCH
Abstract –
Automatic helmet and number plate detection systems are at the forefront of modern traffic management and law enforcement. Leveraging cutting-edge computer vision and machine learning technologies, these systems offer real-time identification and tracking of helmets worn by motorcycle riders and number plates on vehicles. This abstract provides an overview of their significance, working principles, and implications for road safety. These systems are designed to enhance road safety by swiftly identifying and addressing violations. For helmets, they detect instances where riders are not wearing protective headgear, allowing authorities to enforce safety regulations. On the other hand, number plate recognition supports law enforcement by aiding in vehicle identification, tracking, and incident investigation. The efficiency of automatic detection systems lies in their real-time operation, reducing response time and the risk of human error. Furthermore, they facilitate data collection that can be invaluable for traffic analysis, monitoring, and decision-making. Challenges include ensuring high accuracy, robustness under varying conditions, and adaptability to different scenarios. Continuous research and development efforts aim to improve the reliability and effectiveness of these systems. As technology advances, automatic helmet and number plate detection systems are poised to play an increasingly vital role in enhancing road safety and traffic management. Their ability to contribute to safer roadways and more effective law enforcement is of paramount importance in the modern world.
KEYWORDS: Helmet Detection, Number Plate Detection, Motorcycle, convolutional neural networks (CNNs), Safety, Deep learning
