mlsanityData to 7 Oct 2026

Multi defect detection and analysis of electron microscopy images with deep learning

arXiv 2108.08883 ↗First posted 19 Aug 2021cs.CV cond-mat.mtrl-scicited by 9

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Abstract (arXiv)

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. This study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 8 of its 52 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

8 of 52 references.

References · 8

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  3. 2019Object Detection in 20 Years: A Surveycited by 528
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  7. 2017ChainerCV: a Library for Deep Learning in Computer Visioncited by 2
  8. 2019Automated Classification of Helium Ingress in Irradiated X-750cited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 3; 2026: 1.

9 papers.

  1. 2025Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterizationcited by 8
  2. 2023Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"cited by 4
  3. 2022Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectivescited by 2
  4. 2024Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoringcited by 1
  5. 2021Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographscited by 0
  6. 2022Machine learning for classifying and interpreting coherent X-ray speckle patternscited by 0
  7. 2025Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forestscited by 0
  8. 2025Improving U-Net Confidence on TEM Image Data with L2-Regularization, Transfer Learning, and Deep Fine-Tuningcited by 0
  9. 2026Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentationcited by 0