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Author(s):

Mr. G. Balamurugan | Ms.Z. Maimuna Ralina
Keywords:

Computer Vision, YOLOv8 Object Detection, Helmet Detection Number Plate Detection.
Abstract:
The Project Titled "Helmet And Number Plate Detection Using Deep Learning" Employs Advanced Computer Vision And Deep Learning Techniques To Enhance Road Safety And Automate Traffic Law Enforcement. The System Is Developed Using Python And Utilizes The YOLOv8 (YouOnly Look Once, Version 8) Architecture — A State-of-the-art Object Detection Model — For Real-time Identification Of Helmets And Vehicle Number Plates. The Web-based Interface Is Created Using HTML, CSS, And JavaScript, Supported By The Flask Framework, Ensuring Responsive And User-friendly Interaction. The YOLOv8 Model Is Trained On A Comprehensive Dataset Containing Various Road Scenes Under Diverse Lighting, Weather, And Camera Conditions. The Model Achieved A Training Accuracy Of88% And A Validation Accuracy Of 79%, Indicating Strong Performance In Detecting Both Helmets And Number Plates. The System Operates In Three Modes: Image Mode, Video Mode, And Web Camera Mode, Providing Flexibility For Static And Real-time Detection. It Automates The Identification Process, Helping Traffic Authorities Monitor Compliance And Improve Public Safety. This Project Thus Demonstrates The Effective Integration Of Deep Learning With Computer Vision For Intelligent Transportation Monitoring And Enforcement.