Deep-learning-based anomaly detection significantly facilitates the automated surface inspection for, e.g., detection and segmentation of defects. The technology is able to unerringly and independently localize deviations, i.e., defects of any type, on subsequent images.
You only need a low number of high-quality images for training because defects of varying appearance can be detected without any previous knowledge or any preceding labeling efforts. Training a new network can mostly be done in a matter of seconds, allowing users to perform many iterations to fine-tune their application without sacrificing a lot of precious time. Furthermore, the inference is also very fast.