期刊
IET IMAGE PROCESSING
卷 13, 期 9, 页码 1516-1524出版社
WILEY
DOI: 10.1049/iet-ipr.2019.0404
关键词
neural nets; carbon steel; construction industry; learning (artificial intelligence); metallurgy; steel; image segmentation; deep learning approach; plain carbon steel microstructure images; grade; quality customised; construction industry; transportation; quality; grade; specific heat treatment procedures; specific desired properties; computer-based simulations; metallurgy industry; manual experimentation errors; metal heat treatment processes; digital microstructure images; suitable forms; optimal digital forms; simulation models; raw metal microstructure image; Generative Adversarial Network architecture; steel microstructure image segmentation; authors; GAN model; conventional deep learning models; annotated ground truth segmentation masks; sufficient segmented steel microstructure images; sufficient ground truths generation; segmentation network training; related metal microstructure image processing researches; experiments
类别
资金
- Science and Engineering Research Board (SERB), Department of Science and Technology (DST) [ECR/2016/000040]
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.
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