title	emoji	colorFrom	colorTo	sdk	app_port	tags	pinned	short_description	license
SourceCode	👀	red	red	streamlit	8501	streamlit	false	Paranox 2.0 Hackathon	mit

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🚦 SmartLane AI – Intelligent Traffic & Emergency Vehicle Management System

📅 Date: November 16, 2025
🏁 Event: TechXNinjas – PARANOX 2.0 (3-Month National Level Innovation + 24-Hour Build Hackathon)
👥 Team: Team SourceCode
🌍 Live Demo: https://paranox-sourcecode.hf.space
🧠 CNN Model Training Notebook: https://colab.research.google.com/drive/1v-TD755AdnonZuIX0nXLbM2DvF281xiu?usp=sharing

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🔥 About SmartLane AI

SmartLane AI is an AI-powered Traffic Intelligence Platform designed to optimize 4-way traffic intersections using:

🚘 YOLOv8 Object Detection
🚑 Multimodal Ambulance/Emergency Vehicle Detection
🎯 CNN Deep Learning Classification
📊 Real-Time Traffic Analytics & Signal Time Optimization

It automatically prioritizes emergency vehicles (Ambulance) by extending green light duration and clearing the lane using a multi-method decision model:

Detection Layers (Priority Order)
1️⃣ Manual Override Control
2️⃣ YOLO + Color Signature Analysis
3️⃣ CNN Ambulance Classifier
4️⃣ Red/White Pattern Recognition
5️⃣ Emergency Text/OCR Pattern Detection

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🧪 Live Demo & Results

🔗 Live Web App: https://paranox-sourcecode.hf.space
📌 Upload four road images (North, East, South, West) → SmartLane AI detects vehicle density and prioritizes emergency lanes.

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🎯 Key Features

Feature	Description
🚗 Vehicle Detection	YOLOv8 counts Cars, Buses, Trucks, Motorcycles
🚑 Emergency Recognition	CNN + YOLO + Color + Text Pattern Fusion
🚦 Adaptive Traffic Signal Control	AI-driven timing & clearance logic
🟢 Emergency Priority Mode	Automatically extends green light (35s)
📊 Traffic Intelligence Dashboard	Vehicle matrix, charts, priority rankings
📥 Export Reports	Download .txt traffic analysis summary

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🛠 Technology Stack

Category	Tools
AI / CV Models	YOLOv8, TensorFlow CNN
Framework	Streamlit
Language	Python
Visualization	Matplotlib, Pandas
Image Processing	OpenCV, PIL

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📦 Project Structure

SMARTLANE-AI/
│
├── app.py                      # Main Streamlit Application
├── ambulance_cnn_final.keras   # Trained CNN Model
├── model/                      # Additional model assets (optional)
│   └── classes.txt
├── README.md
└── requirements.txt

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/lovnishverma/SMARTLANE-AI.git
cd SMARTLANE-AI

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run SmartLane AI

streamlit run app.py

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🧠 CNN Ambulance Classifier

The system includes a custom-trained CNN for Ambulance vs Non-Ambulance classification.

📌 Training Notebook (Google Colab): ➡ https://colab.research.google.com/drive/1v-TD755AdnonZuIX0nXLbM2DvF281xiu?usp=sharing

Model Summary:

• Input: 192×192 RGB Image
• Output: Softmax (Ambulance / Non-Ambulance)
• Accuracy: ≈ 99.2% on Validation

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🚦 AI Signal Timing Logic

Condition	Green Time
Emergency Vehicle	⏱ 35 seconds
Normal Traffic Base	⏱ 5 seconds
Extra Time	➕1 sec / 2 vehicles
Max Green	⏳ 25 seconds

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📍 Hackathon Overview (PARANOX 2.0)

PARANOX 2.0 is a 3-month national-level innovation event + 24-hour build hackathon where students BUILD · PITCH · WIN by converting their prototypes into real-world products. SmartLane AI was developed in this challenge under TechXNinjas.

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👨‍💻 Team SourceCode

Member	Role
👑 Lovnish Verma	Lead Developer / ML Engineer
🧠 Chandan Saroj	Computer Vision & CNN Training
⚡ Prateek Dhar Dwivedi	UI/UX & Streamlit Integration
🛰 Aman Choudhary	Deployment & Optimization

Want to join or collaborate? PRs are welcome!

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🛡 License

This project is licensed under MIT License. You are free to modify and use with attribution.

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⭐ Support the Project

If you find this useful, please star the repo:

👉 https://github.com/lovnishverma/SMARTLANE-AI 🌟

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📞 Contact / Connect

For research or collaboration inquiries:

📧 Email: [email protected] 🌐 GitHub: https://github.com/lovnishverma 🔗 LinkedIn: https://www.linkedin.com/in/lovnishverma/

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🚦 SmartLane AI

Transforming traffic. Saving lives. Intelligent cities start here.