2021
DOI: 10.1016/j.commatsci.2021.110560
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A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations

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Cited by 42 publications
(9 citation statements)
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“…MOT is defined as the task of predicting the trajectories of the objects of interest in videos or image sequences. The current tracking application is restricted to dislocation loop tracking 14 and nanoparticle tracking 21 , 22 that use two-compute intensive separate models for object detection and tracking. The tracking model usually contains a traditional computer vision algorithm with a slow rigid feature Re-ID and association method.…”
Section: Introductionmentioning
confidence: 99%
“…MOT is defined as the task of predicting the trajectories of the objects of interest in videos or image sequences. The current tracking application is restricted to dislocation loop tracking 14 and nanoparticle tracking 21 , 22 that use two-compute intensive separate models for object detection and tracking. The tracking model usually contains a traditional computer vision algorithm with a slow rigid feature Re-ID and association method.…”
Section: Introductionmentioning
confidence: 99%
“…Particularly, the YOLO convolutional neural network has been implemented previously with the purpose of detecting different structures in TEM images, not only particles such as cavities or other deformations on the surface of a specimen that has been radiated with an ion beam [ 10 ]. Because YOLO excels at real time detection, it has been used for in situ detection and tracking of objects found in a video feed from a transmission electron microscope [ 11 ]. It can also be used in tandem with other CNN architectures to obtain an improved instance segmentation model in one-dimensional nanostructures such as nanowire arrays [ 12 ].…”
Section: Introductionmentioning
confidence: 99%
“…Roberts et al developed a network called DefectSegNet to classify crystallographic defects in the diffraction contrast STEM images of structural alloy (Roberts et al 2019), and Lee et al trained a network to locate and identify defects in averaged high-resolution STEM images and to enable strain mapping (Lee et al 2020). Other types of NN have also been used to segment STEM images and classify crystallographic defects, including Faster Regional Convolutions Neural Network (Faster RCNNs) (Shen, Li, Wu, Liu, et al 2021), Mask RCNN (Jacobs et al 2022), and You Only Look Once (YOLO) (Shen, Li, Wu, Yaguchi, et al 2021).…”
Section: Introductionmentioning
confidence: 99%