Defect classification basedon smart analysis of the inspector's impression expressions of defects

In this paper, we describe about classification of a defect image for automatic visual inspection. Conventionally, a machine learning approach has been effectively utilized as a method for determining the quality of a defect candidate image in recent years. However, because the logic constructed in the classifier is black box generally, it is difficult to perform the maintenance and operation by inspectors. Therefore, we propose a method of machine learning based on inspector's impression expressions about a defect image, such as shape, density, texture. The logic constructed by our method is not only easy to understand by inspectors, but also expected to abstract a tacit knowledge of inspection. Experimental results indicate that our approach is applicable to the defects provided from the production line.