TECHNOVA – EcoVision AI

Inspiration

Every day, millions of recyclable materials are discarded because waste is not separated correctly. This not only increases landfill waste but also reduces the efficiency of recycling systems. We wanted to create an AI-powered solution that makes waste segregation simple, accurate, and accessible. Inspired by the vision of sustainable smart cities and Arm's focus on efficient AI, we developed the idea of EcoVision AI—an intelligent edge AI system that helps people dispose of waste responsibly while reducing energy consumption.

What it does

EcoVision AI uses computer vision to identify different types of waste, including plastic, paper, metal, glass, organic waste, and e-waste. The AI model processes images directly on an Arm-powered edge device, providing instant classification without relying on constant internet connectivity.

The system can:

Detect and classify waste in real time.
Recommend the correct recycling or disposal method.
Operate offline using optimized Edge AI.
Sync waste statistics to the cloud for analytics (optional).
Help organizations monitor recycling performance and environmental impact.

How we built it

We designed EcoVision AI around three core technologies:

Computer Vision for waste recognition.
Edge AI optimized for Arm processors to achieve fast and energy-efficient inference.
Cloud Analytics for optional reporting and monitoring.

Our development process includes:

Collecting and labeling images of different waste categories.
Training a lightweight object detection model.
Optimizing the model using quantization and efficient inference techniques for Arm hardware.
Deploying the model on an edge device for real-time waste detection.
Building a dashboard to visualize recycling statistics and waste trends.

Challenges we ran into

Developing an efficient edge AI solution comes with several challenges:

Collecting a diverse and balanced waste image dataset.
Detecting waste under different lighting and environmental conditions.
Distinguishing visually similar waste materials.
Reducing model size while maintaining high accuracy.
Optimizing inference speed and memory usage on resource-constrained Arm devices.
Designing a system that works reliably even without internet connectivity.

These challenges pushed us to focus on efficient AI optimization rather than simply increasing model complexity.

Accomplishments that we're proud of

Designed an AI solution focused on environmental sustainability.
Built an edge-first architecture that minimizes cloud dependency.
Created a concept that combines Edge AI, Cloud AI, and Generative AI.
Prioritized low-power AI inference suitable for Arm-powered devices.
Developed a scalable solution that can be adapted for schools, offices, smart cities, and recycling facilities.
Demonstrated how AI optimization can improve both performance and energy efficiency.

What we learned

This project helped us understand that building AI is not only about achieving high accuracy but also about making AI efficient and practical for real-world deployment.

We learned about: