Group 7: Challenge: 3 - Defect detection in graphene sheets
Group 7: Challenge: 3 - Defect detection in graphene sheets
Challenge 3 was focused on the identification of defects present within graphene sheets. Provided with electron microscopy images of sheets of graphene, the data-set was partitioned into a sample of perfect patches (regions of an image which do not contain defects), defect patches (regions of an image which contain a defect) and a data-set of images which are not edited or partitioned into smaller sections of analysis. The full image is 256 x 256 patches (Figure 1a). The blue (high electron density) corresponds to atoms and the green corresponds to background. In the full image patches, there is a perfect 48 x 48 patches and a defect 48 x 48 patches. By selecting and training an appropriate machine learning model, the goal was the identification of defect regions contained within a whole electron microscopy image of a graphene sheet.
AI3SD, Machine Learning, Summer School
University of Southampton
Osborne, James
61c54d1a-a446-4a88-92c8-eaeeff2eb92b
Nelson, Ellie
4dcd6bc0-ba24-44e3-93b4-74d09a9f287c
Mamo, Edvin
587b8a0b-f1d9-4e26-9f1a-81d3c19c4005
Zhan, Shaoqi
0b42db9d-c128-4706-9d49-eea25dd76f34
Tendyra, Steven
24c28adc-532b-405c-bcd4-793352e7e246
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
5 July 2022
Osborne, James
61c54d1a-a446-4a88-92c8-eaeeff2eb92b
Nelson, Ellie
4dcd6bc0-ba24-44e3-93b4-74d09a9f287c
Mamo, Edvin
587b8a0b-f1d9-4e26-9f1a-81d3c19c4005
Zhan, Shaoqi
0b42db9d-c128-4706-9d49-eea25dd76f34
Tendyra, Steven
24c28adc-532b-405c-bcd4-793352e7e246
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Osborne, James, Nelson, Ellie, Mamo, Edvin, Zhan, Shaoqi and Tendyra, Steven
,
Frey, Jeremy G., Niranjan, Mahesan and Kanza, Samantha
(eds.)
(2022)
Group 7: Challenge: 3 - Defect detection in graphene sheets
(AI4SD-Machine-Learning-Summer-School, 5)
University of Southampton
9pp.
(doi:10.5258/SOTON/AI3SD0248).
Record type:
Monograph
(Project Report)
Abstract
Challenge 3 was focused on the identification of defects present within graphene sheets. Provided with electron microscopy images of sheets of graphene, the data-set was partitioned into a sample of perfect patches (regions of an image which do not contain defects), defect patches (regions of an image which contain a defect) and a data-set of images which are not edited or partitioned into smaller sections of analysis. The full image is 256 x 256 patches (Figure 1a). The blue (high electron density) corresponds to atoms and the green corresponds to background. In the full image patches, there is a perfect 48 x 48 patches and a defect 48 x 48 patches. By selecting and training an appropriate machine learning model, the goal was the identification of defect regions contained within a whole electron microscopy image of a graphene sheet.
Text
MLSummerSchoolReport_Group7
- Version of Record
More information
Published date: 5 July 2022
Venue - Dates:
AI4SD Machine Learning Summer School, University of Southampton, Southampton, United Kingdom, 2022-06-20 - 2022-06-24
Keywords:
AI3SD, Machine Learning, Summer School
Identifiers
Local EPrints ID: 470692
URI: http://eprints.soton.ac.uk/id/eprint/470692
PURE UUID: f7f8ae20-d75c-44bd-84e6-7aec441c212a
Catalogue record
Date deposited: 18 Oct 2022 16:36
Last modified: 17 Mar 2024 03:52
Export record
Altmetrics
Contributors
Author:
James Osborne
Author:
Ellie Nelson
Author:
Edvin Mamo
Author:
Shaoqi Zhan
Author:
Steven Tendyra
Editor:
Mahesan Niranjan
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics