NDLI: Calculation of phase fraction in steel microstructure images using random forest classifier

Content Provider	IET Digital Library
Author	Paul, Angshuman Gangopadhyay, Abhinandan Chintha, Appa Rao Mukherjee, Dipti Prasad Das, Prasun Kundu, Saurabh
Abstract	Proportions of different phases (phase fraction) in the microstructures determine the quality of dual phase (DP) steel. So, calculation of phase fraction in the microstructures of steel samples is important for quality assurance. Manual calculation of phase fraction involves Le Pera etching of steel which is time consuming and dependent on operator efficiency. Calculation of phase fraction from Le Pera etched samples requires cumbersome manual observations. Nital etching is a faster alternative to Le Pera etching. However, due to lack of visually discriminative information, different phases cannot be identified manually from nital images. We propose a novel method for automatic calculation of phase fractions in steel microstructures from nital images using machine learning techniques. We show that regional contour patterns and local entropy (which cannot be evaluated manually) of regions of nital images are related to the formation process of the phases. We design a method that automatically evaluates regional contour patterns and local entropy from nital images of DP steel. Subsequently, we construct a random forest classifier that uses regional contour patterns and local entropy as features for classification of different phases. Our method is ∼150 times faster than manual classification. Experiments show close to 90% accuracy in classification.
Starting Page	1370
Ending Page	1377
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 8, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1154
Journal	IET Image Processing
Publisher Date	2018-03-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Dual Phase Steel Etching Feature Extraction Image Classification Image Recognition Knowledge Engineering Technique Le Pera Etching Learning in AI Nital Image Phase Fraction Random Forest Classifier Steel Steel Microstructure Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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