Grading of construction aggregate through machine vision: Results and prospects

Authors:
Fionn Murtagh;Xiaoyu Qiao;Paul Walsh;P. A. M. Basheer;Danny Crookes;Adrian Long
Affiliations:
Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK;School of Computer Science, Queen's University Belfast, Belfast BT7 1NN, UK;School of Civil Engineering, Queen's University Belfast, Belfast BT7 1NN, UK;School of Civil Engineering, Queen's University Belfast, Belfast BT7 1NN, UK;School of Computer Science, Queen's University Belfast, Belfast BT7 1NN, UK;School of Civil Engineering, Queen's University Belfast, Belfast BT7 1NN, UK
Venue:
Computers in Industry
Year:
2005

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Abstract

Traditionally, crushed aggregate to be used in construction is graded using sieves. We describe an innovative machine vision approach to such grading. Our operational scenario is one where a camera takes images from directly overhead of a layer of aggregate on a conveyor belt. In this article, we describe effective solutions for (i) image segmentation, allowing larger pieces of aggregate to be measured and (ii) supervised classification from wavelet entropy features, for class assignment of both finer and coarse aggregate.