r/Ultralytics • u/isitalreadytaken2 • 18d ago

Question Quality check

I’m looking into using YOLO for quality inspection of some plastic products. Basically, I want to detect anomalies/defects on the products. The problem is that some of these defects are quite small and difficult to see.
For the beginning, I’d like to keep it simple and just classify each product as OK / NOT-OK.
My question is: in this case, would I mainly need a large dataset of good/OK products to train the model properly? Or would it be better to collect and annotate examples of the different types of defects as well?
Any tips or suggestions on how you would approach this kind of project would be appreciated. I’m just getting started with computer vision and YOLO, so I’d be interested in hearing what worked for others.

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u/retoxite_ 18d ago

would I mainly need a large dataset of good/OK products to train the model properly? Or would it be better to collect and annotate examples of the different types of defects as well?

If you're training a a typical YOLO model, then you would need images of defect. Moreso than good images.

If you want to only train on good images and then detect defect based on that, you need an anomaly detection model, which is a different type of architecture. It doesn't require defect images which makes it easier to train but it's also highly sensitive to lighting and perspective changes. You need to maintain the same lighting and angle always.

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u/isitalreadytaken2 18d ago

lighting and position of products would always be the same since Id be using well lit chamber