Learning defect classifiers for visual inspection images by neuro-evolution using weakly labelled training data
This article presents results from experiments where a detector for defects in visual inspection images was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input statistical data on filter responses from a bank of image filters applied to an image region. Training is done on example images with weakly labelled defects. Experiments show good results of EANT2 in an application area where evolutionary methods are rare.