Grading of construction aggregate through machine vision: Results and prospects

Fionn Murtagh, Xiaoyu Qiao, Paul Walsh, P. A.M. Basheer, Danny Crookes, Adrian Long

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)905-917
Number of pages13
JournalComputers in Industry
Issue number8-9
Early online date10 Oct 2005
Publication statusPublished - 1 Dec 2005
Externally publishedYes


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