In industrial manufacturing processes, quality assurance is an important topic. It is one of the top priorities for Industry 4.0 with a good reason. Defect detection improves the quality, efficiency, and saves lots of money. It is about to become more accessible however this problem faces a number of unique challenges:

It is often difficult to obtain a large amount of anomalous data
The difference between a normal sample and an anomalous sample can be very small
The type of anomalies is not always known beforehand

For this use-case, we want to explore anomaly detection methods which use anomaly-free training data combined with probabilistic AI to detect anomalies. The goal is that all a new client has to do is provide us with a dataset containing non-defective samples and we can build a custom anomaly detection solution for their use case. Recently Intel released the Anomalib library which implements a couple of the current state-of-the-art methods. Some initial exploration of the library has been done by ML6, however there is much more work to be done before we can use it for a client.