Islab: Created page with "{{StudentProjectTemplate |Summary=Few-shot Learning for Quality Inspection |TimeFrame=Fall 2022 |Supervisor=Peyman Mashhadi, Yuantao Fan, |Examiner=Sławomir Nowaczyk |Level=..."

2022-10-21T13:56:48Z

Created page with "{{StudentProjectTemplate |Summary=Few-shot Learning for Quality Inspection |TimeFrame=Fall 2022 |Supervisor=Peyman Mashhadi, Yuantao Fan, |Examiner=Sławomir Nowaczyk |Level=..."

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{{StudentProjectTemplate
|Summary=Few-shot Learning for Quality Inspection
|TimeFrame=Fall 2022
|Supervisor=Peyman Mashhadi, Yuantao Fan,
|Examiner=Sławomir Nowaczyk
|Level=Master
|Status=Draft
}}
There is an increasing interest in intelligent applications for industrial use cases. One area where smart AI-driven applications are particularly interesting is visual inspection of products along the production line. Traditional computer-vision-based approaches rely on large amounts of data to make accurate predictions. Acquiring enough data to achieve a satisfying level of accuracy is often challenging. The aim of this thesis is to develop a tool for quality inspection based on few-shot learning. Few-shot learning refers to the practice of feeding a learning model with a small amount of training data. The goal is to detect anomalies in mounted components on circuit boards. The system should be able to differentiate between defective and functioning boards.

Project with HMS - Revision history

Islab: Created page with "{{StudentProjectTemplate |Summary=Few-shot Learning for Quality Inspection |TimeFrame=Fall 2022 |Supervisor=Peyman Mashhadi, Yuantao Fan, |Examiner=Sławomir Nowaczyk |Level=..."