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Computer Science, Computer Vision and Pattern Recognition, cs.CV, Condensed Matter, Materials Science, cond-mat.mtrl-sci
Abstract:
The inner structure of a material is called microstructure. It stores the
genesis of a material and determines all its physical and chemical properties.
While microstructural characterization is widely spread and well known, the
microstructural classification is mostly done manually by human experts, which
opens doors for huge uncertainties. Since the microstructure could be a
combination of different phases with complex substructures its automatic
classification is very challenging and just a little work in this field has
been carried out. Prior related works apply mostly designed and engineered
features by experts and classify microstructure separately from feature
extraction step. Recently Deep Learning methods have shown surprisingly good
performance in vision applications by learning the features from data together
with the classification step. In this work, we propose a deep learning method
for microstructure classification in the examples of certain microstructural
constituents of low carbon steel. This novel method employs pixel-wise
segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by
max-voting scheme. Our system achieves 93.94% classification accuracy,
drastically outperforming the state-of-the-art method of 48.89% accuracy,
indicating the effectiveness of pixel-wise approaches. Beyond the success
presented in this paper, this line of research offers a more robust and first
of all objective way for the difficult task of steel quality appreciation.
