Seybold Report ISSN: 1533-9211
A.Ravi Kumar
Professor, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, T.S, India, Aravikumar007@gmail.com
M. Akanksha
UG students, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, T.S, India, akankshamailugundla@gmail.com
I. Bhavana
UG students, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, T.S, India.
K. Sirisha
UG students, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, T.S, India, sirishakalapala21@gmail.com
Vol 17, No 07 ( 2022 ) | DOI: 10.5281/zenodo.6877455 | Licensing: CC 4.0 | Pg no: 132-138 | Published on: 25-07-2022
Abstract
Road accidents are becoming more common. With more people buying cars, motorcycles are becoming the main mode of transportation in many countries. Every day, road accidents are increasing. People are also more cautious now, especially with helmetless riders. Serious injuries can result from motorbike accidents. Traffic police are the most common method of ensuring motorcyclists have helmets. They monitor motorcyclists at intersections and through CCTV footage. This requires more human intervention and is a slow process. This paper aims to create an automated system that can detect helmetless riders using concepts such as ML, CNN, and OCR. The vehicle number plate of the helmetless rider is automatically recognized and the characters are extracted. Convolutional Neural Networks are used in this proposed system, which allows for higher accuracy.
Keywords:
CNN, OCR, Convolutional Neural Networks, Number plate Recognition, Helmet detection