AR PLASTIC FOOTPRINT DETECTOR

AR + Environment“See plastic in real time. Save the planet.”“Augmented reality for plastic awareness.”“Visualize plastic. Understand impact.”

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🌍 AR Plastic Detector – Project Story

💡 What Inspired Me

Plastic pollution is one of the biggest environmental problems we face today, yet most plastic waste goes unnoticed in our daily surroundings. I was inspired by the idea of making invisible environmental impact visible. By combining Augmented Reality (AR) with Artificial Intelligence, I wanted to create a system that allows people to see plastic waste in real time and understand its environmental footprint. The goal was not just detection, but awareness and action.

📚 What I Learned

Through this project, I gained hands-on experience in multiple domains:

  • Computer Vision & Deep Learning
    • Object detection using transformer-based models (DETR)
    • Image preprocessing and bounding box prediction
  • Augmented Reality concepts
    • Real-time overlays on camera frames
    • Mapping detection results to screen coordinates
  • Backend Development
    • Building APIs using Django & Django REST Framework
    • Managing sessions, users, and detection history
  • Data Modeling
    • Designing relational models for detections, sessions, and environmental impact
  • Environmental Metrics
    • Estimating CO₂ emissions and water usage per plastic type using: [ \text{CO}2 = \text{Weight}{kg} \times \text{CO}_2_\text{per kg} ]

🛠️ How I Built the Project

The project was developed in multiple layers:

1. Detection Layer

  • Used a pretrained DETR (DEtection TRansformer) model from Hugging Face.
  • Filtered COCO object classes to identify plastic-related items.
  • Estimated plastic type and weight for each detection.

2. AR Visualization

  • Live camera frames are processed in real time.
  • Bounding boxes and labels are overlaid using AR principles to highlight plastic objects.

3. Backend & APIs

  • Built with Django and Django REST Framework.
  • APIs handle:
    • Image uploads
    • Detection results
    • Session tracking
  • Detection data is stored for history and reporting.

4. Environmental Impact Calculation

  • For each detected plastic item: [ \text{Total Impact} = \sum_{i=1}^{n} (\text{weight}_i \times \text{impact factor}) ]
  • Aggregated data per session to show total CO₂ emissions and water usage saved.

5. User Interface

  • Dashboards to view detection history
  • Reports generated daily, weekly, or per session
  • AR interface for live detection

⚠️ Challenges I Faced

  • Real-time performance: Balancing detection accuracy with speed for AR use.
  • Model limitations: COCO dataset is not designed specifically for plastic waste.
  • Data estimation: Approximating plastic weight and environmental impact realistically.
  • System integration: Connecting AR, ML, and backend systems smoothly.
  • Debugging ML + Django: Handling model loading, memory usage, and database access safely.

🚀 Conclusion

This project helped me understand how AI and AR can work together to solve real-world problems. Beyond the technical skills, it taught me how technology can be used as a tool for environmental awareness and social impact. The AR Plastic Detector is a step toward smarter waste management and a cleaner future.

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