SmartBin

After seeing staff members sorting through waste containers during Cal Hacks, our team was inspired to create SmartBin, an automatic system that divides landfill/recycling/compost waste at the source.

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Inspiration

Throughout the hackathon, our team was intrigued by staff members sorting through trash bins in order to ensure recycling, compost and landfill waste was separated. After further investigation and a quick staff member interview, we learned about San Francisco's "zero-waste" program. This program requires event hosts within city limits to sort their waste into the three specified categories to reduce landfill use, with penalties or bans for events that fail to meet the waste sorting criteria. The demand for waste sorting has grown so much that private companies, such as Green Mary, have entered the sector.

Our team came up with an innovative solution, Smart Bin. Smart Bin automates waste sorting, reducing manual labor costs and reducing pollution.

What it does

Smart Bin is a smart, standalone trash disposal system. It scans incoming waste and sorts it as it goes through our system.

How we built it

Our system is built on top of a Raspberry Pi. The Raspberry Pi hosts a Yolo11 object detection system that scans incoming waste and makes an inference as to what section it should be directed to (landfill, compost, recycling). The output from out model is then sent to an Arduino board that controls a servo motor using PMW. Our motor directs the trash to its corresponding bin, effectively sorting it.

Challenges we ran into

One challenge we ran into was the internet connection at the venue. We wanted to download open-source data sets from Roboflow Universe to train our Object Detection model to segregate different types of waste. We also dealt with hardware difficulties during the event, running the servo motors took a lot of juice from the batteries so we had to prepare a lot beforehand. We also modeled designs of our props to be 3d printed during the event, which took a long time to print where we had to optimize different slicing methods to improve from 7 hours of printing to sub 5 hours of print time. We also had limitations when it comes to computing power of the raspberry pi, where all of our detection model, and functions are all ran on this mini-computer.

Accomplishments that we're proud of

We are extremely proud of what we built. We were able to build Smart Bin as a standalone system, one that is only dependent on a Raspberry Pi, an Arduino Board, and a servo motor. The total cost of our unit comes out to less than $50, making it highly accessible and likely to make an impact on the environment.

What we learned

Through this project, we gained a deeper understanding of the complexities involved in waste management and automation. We learned about the real-world implications of waste diversion requirements for events and how crucial it is to accurately sort trash. Additionally, we improved our skills in hardware integration, machine learning, and rapid prototyping, which were crucial to successfully building a working product in a limited time frame.

What's next for SmartBin

Our next steps involve expanding the scalability of the system across urban environments by integrating even more advanced real-time data analytics and optimizing waste collection routes through machine learning algorithms. We aim to enhance the mobile experience by allowing users to locate and interact with SmartBins seamlessly. Additionally, we plan to collaborate with municipalities and large organizations to implement SmartBin on a larger scale, helping cities reduce waste inefficiencies and environmental impact.

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