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 we need to tackle the problem of efficient recycling. The recycling process is composed of 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.
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.
The project aims to implement a resilient software stack for object detection and classification, utilizing cutting-edge methods that deliver real-time results. The solution will need to demonstrate important accuracy in a controlled environment: continuous movement, stable illumination, and non-changing background.
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
Contact
- Valentin Ibars @ WasteFlow – [email protected]
- Mathieu Salzmann @ CVLab – [email protected]