In the past, it has been difficult to inspect vibrant or vivid materials like steel, wood, and fabric for anomalies.
It is extremely difficult to design a traditional, feature-based algorithm to detect anomalies in such materials.
Often a traditional algorithm would have flaws, like too high a false reject rate or a low detection rate on certain types of defects.
With machine learning, this can be trained to a network, based on a large annotated sample set. A network is trained with both anomalies and acceptable sample images.
The optimum state of the machine learning network is when the network is capable of generalizing and thereby detecting anomalies in unprecedented images.