Surface defect recognition of varistor based on deep convolutional neural networks

Xiao, Lei; Gong, Bo; Huang, Lin

doi:10.1117/12.2540562

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…In recent years, there have been publications devoted to the use of deep learning for automatic object recognition in materials science and related fields. For example, a number of studies were aimed at searching for defects in metals [ 12 , 13 , 14 , 15 , 16 ] including images of atomically resolved scanning transmission electron microscopy [ 17 ], classification of objects in scanning electron microscope images [ 18 ], and determining bubbles sizes in thermophysical processes [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

et al. 2020

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Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.

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Section: Introductionmentioning

confidence: 99%

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

et al. 2020

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“…As mentioned above, in recent years, the number of publications devoted to the use of deep learning for automatic object recognition in materials science and related fields is increasing gradually [7,19,[22][23][24][25][26][27][28][29][30]. Of course, due to the practical importance of different types of electron microscopy, developing automatic image processing is very desirable [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

et al. 2022

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Recognition and measuring particles on microscopy images is an important part of many scientific studies, including catalytic investigations. In this paper, we present the results of the application of deep learning to the automated recognition of nanoparticles deposited on porous supports (heterogeneous catalysts) on images obtained by transmission electron microscopy (TEM). The Cascade Mask-RCNN neural network was used. During the training, two types of objects were labeled on raw TEM images of ‘real’ catalysts: visible particles and overlapping particle projections. The trained neural network recognized nanoparticles in the test dataset with 0.71 precision and 0.72 recall for both classes of objects and 0.84 precision and 0.79 recall for visible particles. The developed model is integrated into the open-access web service ‘ParticlesNN’, which can be used by any researcher in the world. Instead of hours, TEM data processing per one image analysis is reduced to a maximum of a couple of minutes and the divergence of mean particle size determination is approximately 2% compared to manual analysis. The proposed tool encourages accelerating catalytic research and improving the objectivity and accuracy of analysis.

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A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications

Khanam,

Hussain,

Hill

et al. 2024

IEEE Access

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Surface defect recognition of varistor based on deep convolutional neural networks

Cited by 4 publications

References 9 publications

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications

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