Abstract :

This Project Presents An AIbased Smart Traffic Violation Detection System Integrated With Machine Learning And Computer Vision Techniques. The System Enhances Road Safety And Traffic Law Enforcement By Automatically Detecting Traffic Violations Through Real-time Video Analysis. It Ensures Accurate, Fast, And Intelligent Decisionmaking Using Artificial Intelligence. The System Analyzes Live Or Recorded Traffic Footage To Identify Violations Such As Helmetless Riding, Signal Jumping, Triple Riding, Overspeeding, And Improper Lane Usage. Object Detection And Image Processing Techniques Are Used To Recognize Vehicles, Traffic Signals, And Road Users, While Machine Learning Algorithms Classify And Validate Violations With High Accuracy. Automated Traffic Violation Detection Helps Reduce Road Accidents, Improve Law Enforcement Efficiency, Minimize Manual Monitoring, And Promote Safer Driving Behavior. The Proposed System Is Developed Using Python, Flask, HTML, CSS, And JavaScript, While Computer Vision Libraries Such As OpenCV And YOLO, Along With Machine Learning Techniques, Are Used For Vehicle Detection And Violation Classification. The Developed System Provides A Simple And User-friendly Interface For Instant Violation Detection, Evidence Generation, And Reporting, Thereby Contributing To Improved Road Safety, Efficient Traffic Management, And Smarter Urban Transportation Systems. KEYWORDS: AI-Based Traffic Violation Detection, Machine Learning, Computer Vision, YOLO, OpenCV, Traffic Monitoring, Road Safety, Smart Transportation, Deep Learning, Intelligent Traffic Management.

Published:

05-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

654 - 661

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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