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Identification of nanocomposites agglomerates in scanning electron microscopy images based on semantic segmentation

Identification of nanocomposites agglomerates in scanning electron microscopy images based on semantic segmentation
Identification of nanocomposites agglomerates in scanning electron microscopy images based on semantic segmentation
The agglomeration causes a significant challenge to nanodielectrics. Identification of agglomerates in scanning electron microscopy (SEM) images is an important step of solving this issue. Motivated by the fast development of image recognition in computer vision, we propose a new approach for agglomerates identification in SEM images of nanodielectrics by semantic segmentation, which is more efficient and accurate than traditional methods. Three models based on convolutional neural networks are investigated
in this work, namely pixel blocks classification network, full convolutional segmentation network employed with data augmentation and unsupervised self‐encoding network. All three networks can preliminarily identify agglomerates of spherical silica-based blend polyethylene nanocomposites. The mean intersection over union (mIoU) of pixel blocks classification network is 0.843 and it takes 25 s to process an image. Full convolutional segmentation network only needs 0.059 s to process a sample, with a mIoU of 0.777. Unsupervised self‐encoding network can reach a mIoU of 0.747 at a speed of 5.806 s. According to the amount of data sets, and requirements for different speed and accuracy, three kinds of networks can be flexibly selected.
dielectric materials, scanning electron microscopy
93 - 103
Bai, Yu
ac52e21f-e6dc-4868-9f5b-503cbdc3c7d4
Wang, Yan
88286e5c-493d-4f20-81dc-66b1c0949bf5
Qiang, Dayuan
2a64f637-fc33-4722-ab29-4e8fd60895a1
Yuan, Xin
bc7a2e7c-7b03-49d9-a4f6-4209133cc0db
Wu, Jiehui
047a3be8-82d1-4f83-9152-ec173bf429f2
Chen, Weilong
f8f84d9b-56cb-446a-805d-bd9732726698
Zhang, Sai
7912c768-3076-4b30-aea8-f8d0da6b6b03
Zhang, Yanru
fb08ddc0-aaaa-4cc7-a37c-5277d9d35a58
Chen, George
3de45a9c-6c9a-4bcb-90c3-d7e26be21819
Bai, Yu
ac52e21f-e6dc-4868-9f5b-503cbdc3c7d4
Wang, Yan
88286e5c-493d-4f20-81dc-66b1c0949bf5
Qiang, Dayuan
2a64f637-fc33-4722-ab29-4e8fd60895a1
Yuan, Xin
bc7a2e7c-7b03-49d9-a4f6-4209133cc0db
Wu, Jiehui
047a3be8-82d1-4f83-9152-ec173bf429f2
Chen, Weilong
f8f84d9b-56cb-446a-805d-bd9732726698
Zhang, Sai
7912c768-3076-4b30-aea8-f8d0da6b6b03
Zhang, Yanru
fb08ddc0-aaaa-4cc7-a37c-5277d9d35a58
Chen, George
3de45a9c-6c9a-4bcb-90c3-d7e26be21819

Bai, Yu, Wang, Yan, Qiang, Dayuan, Yuan, Xin, Wu, Jiehui, Chen, Weilong, Zhang, Sai, Zhang, Yanru and Chen, George (2022) Identification of nanocomposites agglomerates in scanning electron microscopy images based on semantic segmentation. IET Nanodielectrics, 5 (2), 93 - 103. (doi:10.1049/nde2.12034).

Record type: Article

Abstract

The agglomeration causes a significant challenge to nanodielectrics. Identification of agglomerates in scanning electron microscopy (SEM) images is an important step of solving this issue. Motivated by the fast development of image recognition in computer vision, we propose a new approach for agglomerates identification in SEM images of nanodielectrics by semantic segmentation, which is more efficient and accurate than traditional methods. Three models based on convolutional neural networks are investigated
in this work, namely pixel blocks classification network, full convolutional segmentation network employed with data augmentation and unsupervised self‐encoding network. All three networks can preliminarily identify agglomerates of spherical silica-based blend polyethylene nanocomposites. The mean intersection over union (mIoU) of pixel blocks classification network is 0.843 and it takes 25 s to process an image. Full convolutional segmentation network only needs 0.059 s to process a sample, with a mIoU of 0.777. Unsupervised self‐encoding network can reach a mIoU of 0.747 at a speed of 5.806 s. According to the amount of data sets, and requirements for different speed and accuracy, three kinds of networks can be flexibly selected.

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IET Nanodielectrics - 2022 - Bai - Identification of nanocomposites agglomerates in scanning electron microscopy images - Version of Record
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Accepted/In Press date: 13 February 2022
Published date: 4 March 2022
Keywords: dielectric materials, scanning electron microscopy

Identifiers

Local EPrints ID: 470378
URI: http://eprints.soton.ac.uk/id/eprint/470378
PURE UUID: 75e7663f-2acb-453d-8520-900cf03e9046

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Date deposited: 07 Oct 2022 16:34
Last modified: 07 Oct 2022 16:36

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Contributors

Author: Yu Bai
Author: Yan Wang
Author: Dayuan Qiang
Author: Xin Yuan
Author: Jiehui Wu
Author: Weilong Chen
Author: Sai Zhang
Author: Yanru Zhang
Author: George Chen

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