AUTOMATIC PETROGRAPHIC INSPECTION BY USING IMAGE PROCESSING AND MACHINE LEARNING
Katharina Anding, Daniel Garten, André Göpfert, Matthias Rückwardt, Edgar Reetz, Gerhard Linß
Abstract:
In this paper we present a method for an automatic inspection of mineral aggregates. Certain components of aggregates can negatively affect the mechanical strength of produced concrete or asphalt as well as cause the destructive alkali silica reaction. The automatic recognition of such aggregates could be successfully implemented by means of image processing and machine learning algorithms. We achieved a total recognition rate of 88% for this very complex recognition problem. Therefore every step in the pattern recognition process has been successfully optimized. In conjunction with the object singularization we achieved a fully automatic testing instrument for petrographic inspection.
Keywords:
aggregate, petrographic inspection, SVM, machine learning
Download:
IMEKO-WC-2012-TC2-O4.pdf
DOI:
-
Event details
Event name:
XX IMEKO World Congress
Title:

Metrology for Green Growth

Place:
Busan, REPUBLIC of KOREA
Time:
09 September 2012 - 12 September 2012
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