Classification of Masonry Bricks Using Convolutional Neural Networks – a Case Study in a University-Industry Collaboration Project

Abstract

This paper presents a case study - developing a computer-based classification framework to classify masonry bricks into three quality categories - carried out as a part of the Robocoast R&D Center project. The project aims at better collaboration between universities and industry by establishing an innovation platform where companies can bring their challenges to be addressed together with university experts. The project also promotes collaboration between universities being a part of the RoboAI Competence Centre - a joint research and innovation platform of Satakunta University of Applied Sciences (SAMK) and Tampere University, Pori unit. Automatic classification of bricks is important as it is foreseen that a robotic arm, powered by an automatic classifier, could replace the heavy and tedious work currently performed by humans in brick factories. A convolutional neural network-based solution, using a pretrained VGG-16 deep learning architecture, is proposed. Overall accuracy of 88 % was obtained when considering all three quality classes. When only discarding class 3 bricks, i.e., those that are not suitable for any construction work, the accuracy was 93 %.