Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives
Not on the map. Why?
Abstract (arXiv)
Finding quantitative descriptors representing the microstructural features of a given material is an ongoing research area in the paradigm of Materials-by-Design. Historically, microstructural analysis mostly relies on qualitative descriptions. However, to build a robust and accurate process-structure-properties relationship, which is required for designing new advanced high-performance materials, the extraction of quantitative and meaningful statistical data from the microstructural analysis is a critical step. In recent years, computer vision (CV) methods, especially those which are centered around convolutional neural network (CNN) algorithms have shown promising results for this purpose. This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks, including classification, object detection, segmentation, feature extraction, and reconstruction. Additionally, we identified the main challenges with regard to the application of these methods to materials science research. Finally, we discussed some possible future directions of research in this area. In particular, we emphasized the application of transformer-based models and their capabilities to improve the microstructural analysis of materials.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 54 of its 130 references that are in the map. First posted on arXiv 29 Jul 2022.
Builds on
54 of 130 references.
References · 54
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2017Mask R-CNNcited by 7,259
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
38 more
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
- 2014Going Deeper with Convolutionscited by 4,231
- 2013Auto-Encoding Variational Bayescited by 3,980
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
- 2015Learning Deep Features for Discriminative Localizationcited by 2,430
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
- 2013Visualizing and Understanding Convolutional Networkscited by 2,278
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
- 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
- 2018Self-Attention Generative Adversarial Networkscited by 809
- 2014Deep Learning in Neural Networks: An Overviewcited by 765
- 2017Wasserstein GANcited by 635
- 2021Transformer in Transformercited by 632
- 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
- 2018Albumentations: fast and flexible image augmentationscited by 558
- 2019Cross-Modal Self-Attention Network for Referring Image Segmentationcited by 263
- 2017DARLA: Improving Zero-Shot Transfer in Reinforcement Learningcited by 147
- 2021Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformercited by 115
- 2018The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approachescited by 74
- 2019Visualizing Deep Networks by Optimizing with Integrated Gradientscited by 59
- 2020Overview: Computer vision and machine learning for microstructural characterization and analysiscited by 22
- 2018A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictionscited by 21
- 2018High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steelcited by 19
- 2020iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbationscited by 19
- 2020Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materialscited by 18
- 2018Generative Adversarial Network for Medical Images (MI-GAN)cited by 17
- 2019Deep-Learning-Based Image Segmentation Integrated with Optical Microscopy for Automatically Searching for Two-Dimensional Materialscited by 17
- 2017Advanced Steel Microstructural Classification by Deep Learning Methodscited by 15
- 2020Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Datacited by 14
- 2020Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase Contrast Transmission Electron Microscopy Imagescited by 10
- 2021Multi defect detection and analysis of electron microscopy images with deep learningcited by 9
- 2021A deep learned nanowire segmentation model using synthetic data augmentationcited by 7
- 2016Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networkscited by 4
50 most cited of 54 All 54 (JSON)
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.