Every year we generate globally 2 billion tonnes of waste and WorldBank forecast that this number could rise up to 3.4 billion tonnes in 2050. To transform this matter into valuable resources, we need to tackle the problem of efficient recycling. The recycling process comprises multiple steps from waste collection to material transformation, however the most difficult is sorting or recognizing value in a waste mix. At WasteFlow we are developing a Saas for recycling facilities to help them optimize their recycling process by understanding waste mix at different steps of the recycling process.
A sorting facility is composed of multiple sorting machines that will isolate different types of materials. However, when the facility witnesses low performance, it is very complex to understand the source of the problem. Thus, at WasteFlow we are developing a modular solution of cameras that can be placed at multiple strategic points in the sorting process that will analyze sorting performance, and recognize performance loss and material blocking risks.
Through this project, you will be developing a solution for real-world application that will be used in the future within WasteFlow service and will help optimize recycling.
Utilizing a large dataset derived from cameras placed at the input and output of a specific sorting machine, this project aims to create a reliable model that compares object detection and classification at these two junctures. The primary goals of this model include measuring the sorting process’s efficiency all while considering the probabilistic nature of each waste characterization analysis. Furthermore, an avenue for implementation could involve incorporating edge computing into the model to enhance the scalability.
The work will be done in collaboration with a member of WasteFlow team and a research supervisor at CVLAB.
Key Domains:
- Deep Learning
- Object detection
- Recycling
Prerequisite:
- Proficiency in Python
- Basics of Computer Vision and Machine Learning with experience in Pytorch or similar libraries
Preferred:
- Edge Computing Experience
Contact
- Valentin Ibars @ WasteFlow – [email protected]
- Mathieu Salzmann @ CVLab – [email protected]