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<p>The rapid advancements in computer vision (CV) technology have transformed the traditional approaches to material microstructure analysis. This review outlines the history of CV and explores the applications of deep-learning (DL)-driven CV in four key areas of materials science: microstructure-based performance prediction, microstructure information generation, microstructure defect detection, and crystal structure-based property prediction. The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction. Moreover, recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments. The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data, thereby allowing for the discovery and innovation of next-generation materials. Finally, the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.</p>

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From microstructure to performance optimization: Innovative applications of computer vision in materials science

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Chunyu Guo,
  • Xiangyu Tang,
  • Yu’e Chen,
  • Changyou Gao,
  • Qinglin Shan,
  • Heyi Wei,
  • Xusheng Liu,
  • Chuncheng Lu,
  • Meixia Fu,
  • Enhui Wang,
  • Xinhong Liu,
  • Xinmei Hou,
  • Yanglong Hou

摘要

The rapid advancements in computer vision (CV) technology have transformed the traditional approaches to material microstructure analysis. This review outlines the history of CV and explores the applications of deep-learning (DL)-driven CV in four key areas of materials science: microstructure-based performance prediction, microstructure information generation, microstructure defect detection, and crystal structure-based property prediction. The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction. Moreover, recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments. The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data, thereby allowing for the discovery and innovation of next-generation materials. Finally, the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.