A visual-inspection system using a self-organizing map

Abstract

Visual inspections by hand often cause bottlenecks in production processes in industries. Therefore, it is desirable to be mechanized and automated. In order to satisfy these requirements, we apply image recognition using a self-organizing map (SOM) to visual inspection equipment. The SOM maps high-dimensional input data onto a low-dimensional (typically two-dimensional) space. Through the mapping, the data are automatically clustered based on their similarity. Any unknown data which are input onto the self-organized map are also mapped onto it according to their similarity. The categories of the unknown data are thus recognized based on their positions on the map. The reason we use a SOM for inspections is that users can then know the similarity distribution of all data at a glance on the map, and understand the mechanism of the recognition visually. We have developed a visual inspection system using a SOM, and have evaluated it using actual product images. We have obtained high recognition accuracies of 98% and 96% for one- and two-inspection-point tests, respectively, for a real industrial product.