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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.
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