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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 05 | May 2025

p-ISSN: 2395-0072

www.irjet.net

Traffic Violation Detection System Srikanth S R1, Sanjay S M 2, Shrinidhi Rao3, Sukruth Kumar JV4, Dr. Nikitha S5. 1,2,3,4 Students, Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India

Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India ---------------------------------------------------------------------***--------------------------------------------------------------------5Associate Professor,

Abstract - Urban traffic management faces significant

tampering, and promptly notifies the police or emergency services. This system is a proactive tool that reduces errors and resource requirements, not a replacement for human observation. Its adaptability to various settings and number plate layouts, together with its real-time processing capabilities, bridges the gap between traditional traffic control and modern safety standards, offering a scalable platform for smart cities in a world that is becoming more networked by the day.

challenges due to manual enforcement’s inefficiencies and limited real-time capabilities. This study presents a Traffic Violation Detection System, an AI-powered solution that automates the detection of violations such as signal jumping, helmetless riding, triple riding, and no-parking offenses. Leveraging Optical Character Recognition (OCR), YOLObased object detection, machine learning, and image processing, the system integrates IR sensors and cameras to capture and analyze vehicle number plates, issuing SMS notifications to offenders with violation details and fines. Beyond traffic enforcement, it monitors road conditions for accidents and detects suspicious activities, like unauthorized gatherings or vehicle tampering, alerting emergency services or police instantly. Built with open-source tools like Python, OpenCV, and TensorFlow, the system ensures costeffectiveness and scalability, operating robustly across diverse lighting conditions and number plate formats. Its modular design supports multi-lane monitoring and future enhancements, such as integration with smart vehicles or advanced surveillance networks. This tool aims to enhance public safety, reduce accidents, and streamline traffic flow, offering a scalable foundation for smart city initiatives.

1.1 Motivation The growing problem of urban traffic management, characterized by persistent violations and accidents, serves to highlight the necessity of automated solutions to improve road safety. Human-based manual enforcement tends to be inconsistent, slow, and prone to missing violations, especially where traffic is high or resources are low. The Traffic Violation Detection System is motivated by the desire to use intelligent automation to enable consistent, real-time enforcement, taking pressure off traffic police and enhancing public safety. The increasing number of road accidents, compounded by violations such as helmet-less riding and jumping the signal, underscores the need for scalable, technology-based interventions. Furthermore, the system caters to the increasing need for multi-modal surveillance to identify suspicious behavior, including tampering with cars or unauthorized gatherings, which conventional systems fail to detect. With the use of AI and open-source capabilities, the system endeavors to offer an effective, affordable alternative to human monitoring, eliminating errors, and encouraging adherence to traffic rules, leading to more secure roads and intelligent cities.

KeyWords: Traffic violation detection, AI surveillance, number plate recognition, YOLO algorithm, OCR, machine learning, public safety, smart traffic management, image processing, open-source technology 1. INTRODUCTION The sudden urbanization of cities has amplified the traffic management challenges, with manual enforcement being unable to cope with increasing violations and safety issues. With the use of simple digital tools, automation is helping to increase road safety and streamline enforcement. Signal jumping, helmetless riding, triple riding, and no-parking infractions are among the infractions that the proposed Traffic Violation recognition System automatically detects using AI-based technologies such Optical Character Recognition (OCR), YOLO-based object recognition, and picture processing. The system analyses license plates, identifies criminals, and provides real-time SMS messages with infraction details and penalties using infrared sensors and cameras. Along with traffic control, it monitors the state of the roads for accidents and spots unusual activity, such as unapproved gatherings or vehicle

Impact Factor value: 8.315

1.2 Objective

Design an AI-powered platform to identify traffic offenses, such as jumping the signals, helmet-less riding, triple riding, and no-parking, with real-time image processing.

Develop and test various machine learning algorithms, such as YOLO and OCR, for identifying violations and recognizing number plates.

Create an easy-to-use interface so that emergency personnel and traffic cops can examine alerts and violation information.

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