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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 13 Issue: 02 | Feb 2026

p-ISSN: 2395-0072

www.irjet.net

Smart Surveillance For No-Helmet Violations And License Plate Identification Mrs.Y.Uma1 , Jaidi Rishwika2, Kanugula Maneesh Kumar3, Gopagoni Shanmukha4, Adapa Aravind5 1Assistant Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India

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Abstract - Helmet non-compliance among two-wheeler

Deep learning-based object detection algorithms such as YOLO (You Only Look Once) provide real-time detection with high accuracy and can be deployed in practical applications such as helmet detection and number plate recognition. Integrating such technologies into a digital enforcement system can significantly reduce manual effort and improve transparency in challan generation.

riders remains a major cause of severe injuries and fatalities in road accidents. Manual monitoring and challan generation by traffic authorities is time-consuming, inconsistent, and prone to human error, especially in high-traffic areas. To address this issue, this paper presents an AI-Based Helmet Violation Detection and E-Challan Generation System using deep learning and computer vision techniques. The proposed system allows users to upload an image of a two-wheeler rider through a web interface. A YOLO-based object detection model is used to verify the presence of a rider and detect helmet usage. If a helmet violation is identified, the system automatically detects the vehicle number plate using a dedicated YOLO model and extracts the registration number through OCR using EasyOCR. Based on the extracted details, the system generates an electronic challan with a fixed fine amount of ₹235. All challan records, evidence images, timestamps, and generated PDFs are stored in a database for future reference. The system provides a user-friendly interface for result visualization, challan download, and challan history tracking. The proposed solution improves enforcement efficiency, reduces manual workload, and supports scalable integration into smart traffic surveillance systems.

This project proposes an AI-Based Helmet Violation Detection and E-Challan Generation System that automates the process of detecting helmet violations and generating electronic challans. The system accepts an uploaded image of a two-wheeler rider, detects whether the rider is wearing a helmet, and if not, identifies the vehicle’s number plate and extracts the registration number using OCR. A challan is then generated with a predefined fine amount and stored in a database along with evidence and timestamp. The system also provides downloadable PDF challans and challan history for monitoring and record maintenance.

1.1 Motivation

Key words : Helmet Detection, E-Challan Generation, YOLO, Number Plate Recognition, EasyOCR, Computer Vision, Deep Learning, Django, Traffic Rule Enforcement.

Helmet usage plays a vital role in reducing head injuries, yet many riders ignore safety regulations. The motivation behind this work is to create an automated and reliable enforcement system that encourages helmet compliance and enhances road safety.

1. INTRODUCTION

1.2 Problem Statement Existing traffic enforcement methods rely heavily on manual monitoring, which is inefficient and prone to errors. There is a need for an automated system that can detect helmet violations and generate challans accurately with supporting evidence.

Two-wheeler vehicles are one of the most widely used modes of transportation due to their affordability and convenience. However, the rise in two-wheeler usage has also led to an increase in road accidents, many of which result in severe head injuries and fatalities. Wearing a helmet is one of the most effective safety measures for reducing the impact of accidents. Despite strict traffic regulations, helmet non-compliance is still commonly observed, especially in densely populated urban areas. Manual monitoring by traffic police is often limited by manpower, traffic volume, and environmental conditions, which reduces the efficiency of enforcement.

1.3 Objectives The main objectives of the proposed system are to detect the presence of a rider, verify helmet usage, recognize the vehicle number plate, generate an e-challan with fine details, and store all challan records in a database for future reference.

With the advancement of Artificial Intelligence (AI) and Computer Vision, automated systems have become more effective for traffic surveillance and safety rule enforcement.

Impact Factor value: 8.315

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