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:
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.
You can take a headstart when working on this project, as some work has already been done. An actively developed library is available called Anomalib which contains implementations of the current state-of-the-art. However, there is still a gap to be bridged before we can use it in practice. An initial comparison of three algorithms was done however multiple interesting algorithms were excluded.
During this internship you will:
The duration of the internship can be flexible and depends on the candidate preference and the project requirements. The typical duration is 6 to 8 weeks. The preferred duration for this specific project is 6 weeks.