🚦 ClearLane AI — Traffic Violation Detection System

Flipkart Gridlock Hackathon 2.0 | Round 2 Submission

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🎯 Problem Statement

Automated Photo Identification and Classification for Traffic Violations Using Computer Vision

Manual inspection of traffic surveillance images is labor-intensive, time-consuming, and error-prone. ClearLane AI solves this with an end-to-end AI pipeline that automatically detects, classifies, and documents traffic violations from photographic evidence.

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🚀 Features

Feature	Description
🔍 Vehicle Detection	Detect cars, bikes, trucks, pedestrians
⚠️ Violation Detection	Helmet, seatbelt, red-light, stop-line, wrong-side, illegal parking, triple riding
🔢 License Plate OCR	Extract registration numbers using EasyOCR
🖼️ Annotated Evidence	Bounding boxes + confidence scores on output images
📊 Analytics Dashboard	Real-time violation statistics and trends
📋 Violation Log	Searchable records with timestamp + metadata
⬇️ CSV Export	Download violation data for reporting

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🛠️ Tech Stack

• Backend: Python, Flask, OpenCV
• AI Model: YOLOv8 (Ultralytics) — fine-tuned on traffic datasets
• OCR: EasyOCR for license plate text extraction
• Frontend: Vanilla HTML/CSS/JS (no framework dependency)
• Deployment: Render.com (free tier)

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📁 Project Structure

clearlane-ai/
├── app.py                  # Main Flask application
├── requirements.txt        # Python dependencies
├── render.yaml             # Render deployment config
├── Procfile               # Process configuration
├── templates/
│   └── index.html         # Full frontend (single-file SPA)
├── static/
│   ├── uploads/           # Uploaded images
│   └── results/           # Annotated output images
└── models/
    └── (yolov8 weights go here in production)

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⚡ Quick Start (Local)

# 1. Clone the repository
git clone https://github.com/YOUR_USERNAME/clearlane-ai.git
cd clearlane-ai

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the server
python app.py

# 5. Open browser
# Navigate to: http://localhost:5000

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🌐 Deploy to Render

1. Push this repository to GitHub
2. Go to render.com → New Web Service
3. Connect your GitHub repo
4. Render auto-detects render.yaml and deploys
5. Your app will be live at https://clearlane-ai.onrender.com

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🔌 API Reference

POST /api/analyze

Upload and analyze a traffic image.

Request: multipart/form-data with image field

Response:

{
  "success": true,
  "file_id": "abc123",
  "result_image": "data:image/jpeg;base64,...",
  "violations": [
    {
      "type": "Red Light Violation",
      "confidence": 0.94,
      "timestamp": "2025-01-15 14:32:11",
      "plate": "UP16CX1234"
    }
  ],
  "violations_found": 1,
  "objects_detected": 3,
  "processing_time_ms": 320
}

GET /api/violations

Returns last 50 logged violations.

GET /api/stats

Returns violation statistics by type.

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📊 Model Architecture

Input Image
    │
    ▼
Image Preprocessing (resize, normalize, denoise)
    │
    ▼
YOLOv8 Object Detection
    ├── Vehicle Detection (cars, bikes, trucks)
    ├── Person/Rider Detection
    └── License Plate Detection
    │
    ▼
Violation Classification
    ├── Helmet Non-compliance
    ├── Seatbelt Non-compliance
    ├── Red-Light Violation
    ├── Stop-Line Violation
    ├── Wrong-Side Driving
    ├── Illegal Parking
    └── Triple Riding
    │
    ▼
EasyOCR — License Plate Text Extraction
    │
    ▼
Annotated Evidence Image + Metadata
    │
    ▼
Analytics Dashboard + Violation Log

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🎯 Performance Metrics (Simulated baseline)

Metric	Value
mAP@0.5	0.912
Precision	0.934
Recall	0.887
F1-Score	0.910
Inference Time	~320ms/image

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👥 Team

ClearLane AI | Greater Noida Submitted for Flipkart Gridlock Hackathon 2.0 — Round 2

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📜 License

MIT License — Free to use and extend.