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Thesis work: Computer Vision for improvement of manufacturing process
Volvo Business Services AB, Göteborg
- Notera att ansökningsdagen för den här annonsen kan ha passerat. Läs annonsen noggrant innan du går vidare med din ansökan.
Thesis background
Volvo Group Digitalization & IT at Tuve modernizes IT system landscape, integrates data from legacy silos systems and automates business processes. Now it’s time to increase utilization of smart sensors, Big Data, cloud computing and Business Intelligence to monitor, model and simulate manufacturing processes. In our journey towards Digital Factory we want to adopt various advanced technologies like Artificial Intelligence, Machine Learning and Computer Vision.
Thesis scope of work
In this master thesis, you will develop models that could be used to support manufacturing processes at Volvo trucks’ factory in Gothenburg. The work will start with revision of production stations and choosing those where Computer Vision can improve quality, cost efficiency, safety or reduce environmental impact.
Some example use cases to evaluate might be:
- detect parts with defects
- validate if all parts have been correctly assembled or packed
- identify parts
- find incorrectly stored packages
- check if most dangerous zones are entered by employees wearing safety clothes or are not entered at all
- track incoming/outgoing trucks and position of shipping containers at the factory’s yard
The work includes preparation of image datasets and evaluation of different model learning approaches (with various pre-trained models, epochs, datasets) for stationary cameras/computers and mobiles (depending on use case, usability and performance)
Required skills:
- Courses in Machine Learning, Computer Vision, Data Science, Software Engineering or similar.
- At least basic knowledge of machine learning models
- Programming in Python with some experience of relevant frameworks/libraries such as Tensorflow, Pytorch, Keras and OpenCV.
Contact: Roman Dolanski, Delivery Manager Tuve, [email protected]
Thesis Level: Master
Language: English
Number of students: 2
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