2018
DOI: 10.1038/s41524-018-0093-8
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Automated defect analysis in electron microscopic images

Abstract: Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more … Show more

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Cited by 119 publications
(89 citation statements)
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“…However, a large number of images are needed to extract statistically significant information, and recognition is still done manually, which is not only time‐consuming but also inconsistent. Recently, Li et al obtained information about the size and type of defects by combining ML, computer vision, and image analysis techniques (Figure ) . At present, the performance of the program is consistent with the manual analysis of quality.…”
Section: Ai Applications For Materials Science and Engineeringmentioning
confidence: 55%
See 1 more Smart Citation
“…However, a large number of images are needed to extract statistically significant information, and recognition is still done manually, which is not only time‐consuming but also inconsistent. Recently, Li et al obtained information about the size and type of defects by combining ML, computer vision, and image analysis techniques (Figure ) . At present, the performance of the program is consistent with the manual analysis of quality.…”
Section: Ai Applications For Materials Science and Engineeringmentioning
confidence: 55%
“…Then module III determines the loop shape and size. Reproduced with permission . Copyright 2018, Springer Nature.…”
Section: Ai Applications For Materials Science and Engineeringmentioning
confidence: 99%
“…In the field of materials science, deep neural networks have also been receiving increasing attention and have achieved great improvements, for example, in material property prediction and new materials discovery for batteries [25,14] [37,20]. CNNs have also been used for detect analysis on microscopic images of various material surfaces [21,18,22].…”
Section: Machine Learning For Materials Sciencementioning
confidence: 99%
“…Zhang et al [42] use CNN to identify pixels in the pavement image that belongs to a crack, based on local patch information. Later, Li et al [43] developed an algorithm similar to R-CNN on electron microscopic images to study irradiation damage of metal alloys, which uses object detection to propose a damage region first and then categorizes damage types with a CNN classifier. However, these learning-based methods have been mainly applied to 2D images, where cracks are sparsely located on a solid background with minor noise or texture.…”
Section: Introductionmentioning
confidence: 99%