NDLI: Deep learning approach for segmentation of plain carbon steel microstructure images

Content Provider	IET Digital Library
Author	Panda, Aditi Naskar, Ruchira Pal, Snehanshu
Abstract	To bring about variation in the physical and structural properties or grade of a metal, it is made to undergo specific heat treatment procedures; which can be customized to make the metal microstructure evolve desirably, to obtain specific targeted properties. Recently, computer-based simulations of such heat treatment procedures have become popular, however, such simulations are feasible only if the digital microstructure images are available in suitable forms (optimal digital forms of the microstructure images means the distinct grains identified and the grain boundaries demarcated, i.e., segmentation of microstructure images). To this end, the authors propose a deep learning based Generative Adversarial Network (GAN) architecture for steel microstructure image segmentation. The authors’ experimental results prove the performance efficiency of the proposed GAN model, as compared to the state-of-the-art. However, the proposed network architecture requires large volumes of training data, in the form of annotated ground truth segmentation masks. The current literature lacks sufficient segmented steel microstructure images for this training, to the best of their knowledge. Hence, their second contribution in this study is the development of a Convolutional Neural Network-based framework for sufficient ground truths generation, to aid in the proposed segmentation network training.
Starting Page	1516
Ending Page	1524
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 9, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0404
Journal	IET Image Processing
Publisher Date	2019-05-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Annotated Ground Truth Segmentation Masks Authors Carbon Steel Computer Vision And Image Processing Technique Computer-based Simulations Construction Industry Conventional Deep Learning Model Deep Learning Approach Digital Microstructure Image Experiments GAN Model Generative Adversarial Network Architecture Grade/quality Customised Image Segmentation Knowledge Engineering Technique Learning in AI Manual Experimentation Error Metal Heat Treatment Processes Metallurgy Metallurgy Industry Neural Nets Optical, Image And Video Signal Processing Optimal Digital Forms Plain Carbon Steel Microstructure Image Quality/grade Raw Metal Microstructure Image Related Metal Microstructure Image Processing Researches Segmentation Network Training Simulation Model Specific Desired Property Specific Heat Treatment Procedures Steel Steel Microstructure Image Segmentation Sufficient Ground Truths Generation Sufficient Segmented Steel Microstructure Image Suitable Forms Transportation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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