Automatic Determination of Reference Pixels Using Middle Layer of Deep Neural Network for Template Matching

Abstract

In the production line, template matching is used as a technique for positioning parts. In template matching, it is a general problem to be high recognition accuracy and high speed processing. So far, to achieve this problem, a method of selecting several% of pixels effective for positioning from a template has been proposed. However, there is a problem that the method can not be applied unless the object has strong features such as edges. In this research, we propose a method to solve this problem by using Deep Neural Network. It is assumed that Deep Neural Network can detect not only strong features of objects but also weak features, because it can detect defects in visual inspection and identify animals. Therefore, we pay attention to the feature map of the feature extraction unit of Deep Neural Network and we make a hypothesis that features which are effective for class identification remain in the feature map. In this research, reference pixels are determined based on this hypothesis. As a result of experiment using 4000 images, we confirmed that the recognition rate of the proposed method is 97.7%, which is about 27% higher than the conventional pixel selection method.