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
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