Enhancing Waste Detection on Conveyor Belts through Generative Models

In this project, we aim to improve the efficiency of waste detection on conveyor belts using advanced computer vision technology. By incorporating innovative generative models and data fusion techniques, we seek to enhance the accuracy of identifying different types of waste materials. This research will contribute to more effective waste sorting processes, ultimately reducing environmental impact, and promoting recycling efforts. By harnessing the power of these technologies, we aim to significantly improve the accuracy of identifying diverse waste materials on conveyor belts. This not only streamlines waste management processes but also promotes environmental sustainability by enhancing recycling efforts. The outcomes of this project will directly benefit our partner organization, Waste Robotics, by providing them with refined and efficient waste detection technologies, reinforcing their commitment to eco-friendly and sustainable waste management solutions.

Faculty Supervisor:

Ali Motamedi

Student:

Partner:

Waste Robotics Inc.

Discipline:

Computer science

Sector:

Manufacturing

University:

École de technologie supérieure

Program: