Abstract
The practical application of image processing for visual inspection is being promoted, but there are some challenges that cannot be detected only by rule-based methods. In this paper, we evaluate rule-based and machine learning methods for visual inspection of casting parts. Rule-based is a method in which an operator interprets the features of an image and sets rules for judgment such as thresholds. Machine learning, on the other hand, is a method that automatically interprets features from a large amount of data and determines decision rules without using explicit instructions. As a result of the comparison, it was found that machine learning is suitable for the visual inspection of cast parts.
