Paper Key : IRJ************642
Author: Shakti Tripathi,Palak Yadav,Dr. Vibha Srivastava
Date Published: 15 Apr 2024
Abstract
Over time, there has been an increase in motorbike accidents in a number of nations. The automated detection of traffic rule violators is a prerequisite for any intelligent traffic system.With a high population density in all of the major cities, motorcycles are one of the primary modes of transportation in a country like India. In India, there are more than 37 million two-wheeler riders.According to statistics, the majority of motorbike riders don't wear helmets in cities or even on highways. Wearing a helmet can reduce a rider's risk of incurring a head injury or catastrophic brain injury in the majority of motorcycle accident scenarios. For the sake of traffic safety, a system for automatically identifying helmets is therefore necessary. As a result, customized object detection models are constructed with a CNN-based algorithm (YOLO). When a helmetless rider is detected, the number plate is collected and the license registration number is identified using an optical character recognition system.The objective of this project is to design a computerized CNN-based detection device with custom-trained models and datasets for helmet authentication. Additionally, the Challan System is a web-based interface intended to assist users with various matters pertaining to managing and tracking traffic infractions. Essentially, the Challan System makes it possible for bike riders and police to have straightforward conversations using a web-based interface. This project concept demonstrates how users can complete tasks more quickly when Challan is available online. The web-based approach aims to minimize paperwork and conventional procedures in order to improve customer satisfaction.
DOI LINK : 10.56726/IRJMETS52280 https://www.doi.org/10.56726/IRJMETS52280