Two Wheeler Traffic Violation And Ticketing System

Abstract

The increasing number of two-wheeler
accidents due to helmet non-compliance has led to
the development of automated traffic violation
detection systems. These systems leverage
computer vision and deep learning techniques to
detect whether a rider is wearing a helmet.
Advanced object detection models such as YOLO
(You Only Look Once) and Convolutional Neural
Networks (CNNs) are commonly used for realtime
monitoring of traffic through surveillance
cameras. When a violation is detected, the system
captures an image of the rider and extracts the
vehicle’s number plate using Optical Character
Recognition (OCR). This information is then
processed to identify the registered owner, and an
automated email notification is sent, informing
them of the violation along with the
corresponding fine details.Such systems are
crucial in ensuring road safety and enforcing
traffic regulations efficiently without the need for
manual intervention. By integrating artificial
intelligence with automated ticketing, law
enforcement agencies can significantly reduce the
rate of helmet violations and promote safer
driving habits. Additionally, these systems can be
further enhanced by incorporating real-time
databases of vehicle registration and driver
information to facilitate seamless fine collection.
The implementation of such automated ticketing
solutions not only minimizes human effort but
also ensures fair and unbiased enforcement of
helmet laws, ultimately reducing the number of
fatalities in road accidents.