A smart system that detects motorcycle riders without helmets in real time using YOLOv8, reads number plates via OCR, generates e-challans, and allows users to view and pay fines through a mobile application. Admins can access statistics including total violations, number plates matched, and unmatched cases. The system also includes a gamified monthly quiz and live monitoring.
Objectives
Key Objectives are:
1. Detect helmet violations in real time using YOLOv8.
2. Automatically read and extract number plates from violations.
3. Generate e-challans automatically and store violation records.
4. Enable a single Android app for users and admins with role-based access.
5. Allow secure payment of challans via JazzCash and Easypaisa.
6. Conduct monthly gamified quizzes to promote safe riding habits.
7. Provide live video feed monitoring for admins.
8. Send notifications to users when challans are issued or payments are due.
Socio-Economic Benefit
Our project provides many socio-economic benefits:
1. Reduces helmet-related accidents and enhances road safety.
2. Promotes compliance with traffic laws through automated detection.
3. Saves manpower by reducing manual enforcement.
4. Enables faster and accurate challan processing.
5. Provides transparent and reliable violation records.
6. Encourages safe riding habits among students and riders.
7. Generates revenue efficiently via digital payments.
8. Increases awareness through gamified learning and quizzes.
9. Helps authorities track helmet compliance trends via statistics.
10. Reduces chances of fraudulent reporting or missed violations.
Methodologies
The system provides several socio-economic benefits that enhance public safety and improve administrative efficiency. By integrating AI-driven automation, it supports traffic authorities through accurate, real-time monitoring while offering citizens a faster, more convenient, and transparent challan management experience. This ultimately reduces manual workload, minimizes human error, and promotes safer riding habits across the community.
1. Train YOLOv8 model on helmet detection dataset.
2. Use OCR to extract number plates from detected violations.
3. Store rider and violation details in Firebase Firestore.
4. Develop Android application using Kotlin & XML for interaction.
5. Integrate secure mobile payment gateways (JazzCash, Easypaisa).
6. Generate e-challans automatically for violations.
7. Implement live video feed for admin monitoring.
8. Provide notifications and alerts to users for challans and payments.
9. Design gamified monthly quizzes with discounts as incentives.
10. Collect and present statistical insights: total violations, matched/unmatched number plates.
Outcome
The project delivers impactful results that strengthen traffic enforcement, improve user engagement, and enhance overall road safety. These outcomes highlight the efficiency, accuracy, and user-centered design of the entire system.
? Real-time helmet violation detection with high accuracy:
The system uses a trained YOLOv8 model to instantly identify riders without helmets from live video feeds, ensuring quick and precise detection even in dynamic environments.
? Automated e-challan generation and record maintenance:
Once a violation is detected, the system automatically extracts the number plate, generates an e-challan, and stores all associated details such as rider info, time, date, and status in Firebase for permanent tracking.
? User-friendly Android app for both users and admins:
A single mobile application designed with Kotlin & XML enables smooth navigation, providing role-based access where users can manage challans and admins can monitor system activities.
? Secure online payment of challans:
The app integrates with local mobile payment gateways like JazzCash and Easypaisa, allowing users to pay fines securely and instantly, with automatic status updates after successful transactions.
? Gamified quizzes to increase traffic safety awareness:
The monthly quiz module engages users with safety-related questions, encouraging learning while rewarding top performers with challan discounts to promote responsible riding.
? Admin dashboard with total violations, number plates matched and unmatched:
Administrators can view detailed statistics, including total violations recorded, successful number plate matches, unmatched plates, and trends over time, supporting better decision-making.
? Live video monitoring to track ongoing violations:
Admins can monitor real-time video streams directly from the app, allowing them to observe current traffic conditions and system performance without any external tools.
? Detailed notification system for users regarding issued challans:
Users receive instant alerts for new challans, payment confirmations, quiz availability, and updates, ensuring timely awareness and improved user engagement.
? Reliable and transparent violation reporting:
Each violation is logged with visual evidence and metadata, maintaining transparency and preventing disputes, while also creating a traceable digital record.
? Enhanced law enforcement efficiency and improved road safety:
By automating detection, recordkeeping, and communication, the system reduces the burden on traffic staff, improves enforcement accuracy, and ultimately contributes to safer roads and fewer helmet-related injuries.
