ISIJ International
Online ISSN : 1347-5460
Print ISSN : 0915-1559
ISSN-L : 0915-1559
Instrumentation, Control and System Engineering
Development of High Accuracy Segmentation Model for Microstructure of Steel by Deep Learning
Fumito AjiokaZhi-Lei WangToshio OgawaYoshitaka Adachi
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2020 Volume 60 Issue 5 Pages 954-959

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Abstract

We studied on automation of segmentation using deep learning, which has been remarkably developed in recent years.

For the microstructural image of ferrite-martensite dual phase steel, we tried to segment the ferrite phase, martensite phase, and ferrite grain boundary in different colors individually. We created two models, SegNet and U-Net that can perform segmentation with high accuracy and compared the accuracy with an existing method.

As a result, we demonstrated that models using deep leaning is more accurate than the existing method. In particular, U-Net model shows highly accuracy of segmentation for material microstructures.

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© 2020 by The Iron and Steel Institute of Japan
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