A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations

• URL: http://arxiv.org/abs/2108.08882v1
• Date: Thu, 19 Aug 2021 19:15:44 GMT
• Title: A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations
• Authors: Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley, Kevin G. Field, and Dane Morgan
• Abstract summary: We develop an automated TEM video analysis system for microstructural features based on the advanced object detection model called YOLO. Results show that the system can achieve human comparable performance with an F1 score of 0.89 for fast, consistent, and scalable frame-level defect analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Videos captured using Transmission Electron Microscopy (TEM) can encode details regarding the morphological and temporal evolution of a material by taking snapshots of the microstructure sequentially. However, manual analysis of such video is tedious, error-prone, unreliable, and prohibitively time-consuming if one wishes to analyze a significant fraction of frames for even videos of modest length. In this work, we developed an automated TEM video analysis system for microstructural features based on the advanced object detection model called YOLO and tested the system on an in-situ ion irradiation TEM video of dislocation loops formed in a FeCrAl alloy. The system provides analysis of features observed in TEM including both static and dynamic properties using the YOLO-based defect detection module coupled to a geometry analysis module and a dynamic tracking module. Results show that the system can achieve human comparable performance with an F1 score of 0.89 for fast, consistent, and scalable frame-level defect analysis. This result is obtained on a real but exceptionally clean and stable data set and more challenging data sets may not achieve this performance. The dynamic tracking also enabled evaluation of individual defect evolution like per defect growth rate at a fidelity never before achieved using common human analysis methods. Our work shows that automatically detecting and tracking interesting microstructures and properties contained in TEM videos is viable and opens new doors for evaluating materials dynamics.

Related papers

• Real-Time Anomaly Detection in Video Streams [0.0]
  This thesis is part of a CIFRE agreement between the company Othello and the LIASD laboratory. The objective is to develop an artificial intelligence system that can detect real-time dangers in a video stream.
  arXiv  Detail & Related papers  (2024-11-29T14:24:33Z)
• Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems [49.11170948406405]
  We propose an unsupervised method to estimate the physical parameters of known, continuous governing equations from single videos. We take the field closer to reality by recording Delfys75: our own real-world dataset of 75 videos for five different types of dynamical systems.
  arXiv  Detail & Related papers  (2024-10-02T09:44:54Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures [6.483318568088176]
  PiML method integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures. Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results. Result handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
  arXiv  Detail & Related papers  (2024-02-28T02:16:03Z)
• Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements [83.5820690348833]
  We present a framework for low-level vision tasks that does not require any external training data corpus. Our approach learns neural modules by optimizing over a corrupted sequence, leveraging the weights of the coherence-temporal test and statistics internal statistics.
  arXiv  Detail & Related papers  (2023-12-13T01:57:11Z)
• Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection [103.92970668001277]
  We propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection. We first propose a multi-scale temporal modeling module, capable of extracting features from segments of varying lengths. Then, we design a dynamic erasing strategy, which dynamically assesses the completeness of the detected anomalies.
  arXiv  Detail & Related papers  (2023-12-04T09:40:11Z)
• Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data" [0.0]
  In-situ TEM experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed.
  arXiv  Detail & Related papers  (2023-07-12T17:37:46Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation [4.345882429229813]
  Ridge detection is a tool to extract curvilinear features in image processing. We harness temporal correlation across frames through simultaneous analysis of long image sequences. Our approach is specially tailored to handle temporal analysis of objects that seemingly vanish.
  arXiv  Detail & Related papers  (2023-02-02T01:37:43Z)
• Using scientific machine learning for experimental bifurcation analysis of dynamic systems [2.204918347869259]
  This study focuses on training universal differential equation (UDE) models for physical nonlinear dynamical systems with limit cycles. We consider examples where training data is generated by numerical simulations, whereas we also employ the proposed modelling concept to physical experiments. We use both neural networks and Gaussian processes as universal approximators alongside the mechanistic models to give a critical assessment of the accuracy and robustness of the UDE modelling approach.
  arXiv  Detail & Related papers  (2021-10-22T15:43:03Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework [1.3858051019755282]
  Failure in brittle materials led by micro- to macro-cracks under repetitive or increasing loads is often catastrophic. We develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels.
  arXiv  Detail & Related papers  (2020-03-24T17:13:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.