StreamSight: Deep Learning Techniques for Managing Contaminants in Residential Curbside Recycling

This project works to classify contaminants found in residential curb-side recycling. This is done automatically using computer vision techniques. As recycling is tipped into the recycling truck, cameras take pictures of the recycling and computer vision software works to identify contaminants found in the load. With municipalities equipped with this fine-grained data, they will have the ability to produce targeted education campaigns to improve the recycling process and reduce contamination found in recycling. Image data collected from household recycling will be stored securely and images are not captured with the household’s address or any personally identifiable information connected with the image.

Faculty Supervisor:

Mohamed Eldarieby

Student:

Raymond Knorr;Noah Rowbotham

Partner:

Prairie Robotics Inc.

Discipline:

Engineering

Sector:

Administrative and support, waste management and remediation services

University:

University of Regina