Image analysis for the sorting of brick and masonry waste using machine learning methods

Abstract

This paper describes different machine learning methods for recognizing and distinguishing brick types in ma­sonry debris. Certain types of bricks, such as roof tiles, facing bricks and vertically perforated bricks can be reused and recycled in different ways if it is possible to separate them by optical sorting. The aim of the research was to test different classification methods from machine learning for this task based on high-resolution images. For this purpose, image captures of different bricks were made with an image acquisition system, the data was pre-processed, segmented, significant features selected and different AI methods were applied. A support vec­tor machine (SVM), multilayer perceptron (MLP), and k-nearest neighbor (k-NN) classifier were used to classify the images. As a result, a recognition rate of 98 % and higher was achieved for the classification into the three investigated brick classes