| 引用本文: | 班晓娟,宿彦京,谢建新.深度学习在材料显微图像分析中的应用与挑战[J].材料科学与工艺,2020,28(3):68-75.DOI:10.11951/j.issn.1005-0299.20200140. |
| BAN Xiaojuan,SU Yanjing,XIE Jianxin.Applications and challenges of deep learning in material microscopic image analysis[J].Materials Science and Technology,2020,28(3):68-75.DOI:10.11951/j.issn.1005-0299.20200140. |
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| 深度学习在材料显微图像分析中的应用与挑战 |
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班晓娟1,2,3,宿彦京1,4,谢建新1,4
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(1.北京科技大学 北京材料基因工程高精尖创新中心,北京 100083;2.材料领域知识工程北京市重点实验室(北京科技大学), 北京 100083;3.北京科技大学 计算机与通信工程学院,北京 100083;4. 北京科技大学 新材料技术研究院,北京 100083)
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| 摘要: |
| 材料的组织结构主要受成分和制备加工工艺的影响,是决定材料性能的关键因素,在材料研发的全周期内具有重要作用。材料组织结构以非结构化图像数据的形式呈现,利用人工经验性的手段进行分析和信息抽取,遗漏了大量的材料学信息和隐含知识。深度学习技术的发展和应用,为材料显微图像中信息的精准、快速、自动获取提供了重要的研究手段。本文从图像处理、图像分析和图像理解3个方面概述了材料显微图像处理与信息挖掘的主要研究内容和关键技术,详细介绍了深度学习在图像分析中的图像识别、图像分割和图像生成3个任务中的研究进展,讨论了深度学习在材料显微图像分析和信息挖掘中的发展方向和挑战。 |
| 关键词: 材料显微图像分析 机器学习 深度学习 计算机视觉 图像处理 |
| DOI：10.11951/j.issn.1005-0299.20200140 |
| 分类号:TPB391 |
| 文献标识码:A |
| 基金项目:国家重点研发计划项目(2016YFB0700500). |
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| Applications and challenges of deep learning in material microscopic image analysis |
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BAN Xiaojuan1,2,3, SU Yanjing1,4, XIE Jianxin1,4
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(1.Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China; 2.Beijing Key Laboratory of Knowledge Engineering for Materials Science (University of Science and Technology Beijing), Beijing 100083, China; 3.School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; 4.Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China)
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| Abstract: |
| The structure of materials is mainly affected by the composition and processing technology, which is the key factor to determine the performance of materials and plays an important role in the whole cycle of material research and development. The material microstructure is presented in the form of unstructured image data, which is usually analyzed and extracted by means of artificial experience, leaving out a lot of material information and implicit knowledge. The development and application of deep learning technology provide an important research means for the accurate, fast, and automatic acquisition of information in material microscopic image. In this work, the main research contents and key technologies of material microscopic image processing and information mining were summarized from three aspects of image processing, image analysis, and image understanding. The research progress of deep learning in image recognition, image segmentation, and image generation in image analysis was introduced in detail. The development directions and challenges for the application of deep learning in material microscopic image analysis and information mining were also discussed. |
| Key words: material microscopic image analysis machine learning deep learning computer vision image processing |
