Asbestos Detection Method in Building Materials by Integration of Various Classifiers

Abstract

We propose a method of detecting asbestos in building materials by integrating various kinds of classifiers. Recently, asbestos-related illnesses have become a nation wide problem in Japan. Now, human inspectors check whether asbestos is contained in building materials. An asbestos detection method using a support vector machine (SVM) with a weight summation kernel of color and shape has been proposed. It was effective but it did not work well for asbestos with very thin and low contrast because only a single detector with 40×40 pixels was used. Since the color, shape and size of asbestos vary in microscope images, it is difficult to detect them with a single classifier. Therefore, we train many classifiers with various region sizes and feature types, and integrate them along the orientation of asbestos. We collect the asbestos detection with high accuracy and a small number of false positives by considering the asbestos orientation in classifier integration.