Inspiration
- Everyone can contribute to helping the environment by recycling at almost no cost, but this is limited by lack of knowledge of recyclability of waste items
- Many smart bin solutions based on object detection and item separation have high set up cost, or inconveniences users with sensor and mechanical lags
- Aim is to better inform users about recyclability of waste items, without becoming obtrusive, disruptive or inconvenient
- Build better recycling habits through encouragement and positive incentivisation
- Companies are keen to support eco-friendly initiatives – can promote partnerships with companies through reward schemes
- New waste items/packaging arrive daily, so traditional rulebased classification methods not sufficient. We need a generalised data-driven method (hence DNN).
What it does
- Hardware setup: user mounted device, cheap, easy to install device on the inside of a bin lid or rim
- User signs up to Recyclify service/download app
- after each waste item is thrown in the bin, device keeps track of whether items are correctly recycled or not....
- User received push notifications if items are incorrectly disposed with a gentle reminder for the next time, and encouraging messages for
- user accumulates reward points based on consistent/consecutive recycling habits
- reward points can be redeemed for discount vouchers/loyalty points with partnered companies
How It’s Built
- Raspberry Pi based camera captures and uploads an image stream to a Microsoft Azure hosted container
- Server monitors incoming images, detecting when new objects enter using a structural similarity index between frames
- Flagged images are passed to a DNN to classify the type of product and recyclability
- Updates SQL based customer reward points database hosted by Azure and apushes notifications or emails when a reward is earned or item(s) are incorrectly recycled/binned
Challenges Encountered
- Hardware reliability was a major challenge, with the Raspberry Pi
- Sensitivity of ML model to natural variations in image quality/lighting etc.
Accomplishments that I'm proud of
- Able to produce a proof of concept end-to-end pipeline
- Tackle shortcomings of ML models with classical image processing
What I learned
- Azure container, SQL database and VM
- Circumventing limitations in hardware and data availability
What's next for Recyclify
- ios/web-app development
- Approaching business partnerships (reward points, vouchers etc.)
- Accumulate further data and more training to predict the recyclability of out-of-training samples
- Refining the hardware design to for easy mounting, space and cost efficiency
