The University of Southampton
University of Southampton Institutional Repository

Determination of size of urban particulates from occluded scattering patterns using deep learning and data augmentation

Determination of size of urban particulates from occluded scattering patterns using deep learning and data augmentation
Determination of size of urban particulates from occluded scattering patterns using deep learning and data augmentation
Deep learning has shown recent key breakthroughs in enabling particulate identification directly from scattering patterns. However, moving such a detector from a laboratory to a real-world environment means developing techniques for improving the neural network robustness. Here, a methodology for training data augmentation is proposed that is shown to ensure neural network accuracy, despite occlusion of the scattering pattern by simulated particulates deposited on the detector's imaging sensor surface. The augmentation approach was shown to increase the accuracy of the network when identifying the geometric Y-dimension of the particulates by ~62% when 1000 occlusions of size ~5 pixels were present on the scattering pattern. This capability demonstrates the potential of data augmentation for increasing accuracy and longevity of a particulate detector operating in a real-world environment.
Deep learning, Optics, Particulate matter, Pollution, Sensing
2515-7620
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Loxham, Matthew
8ef02171-9040-4c1d-8452-2ca34c56facb
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, Praeger, Matthew, Loxham, Matthew, Eason, R.W. and Mills, Benjamin (2021) Determination of size of urban particulates from occluded scattering patterns using deep learning and data augmentation. Environmental Research Communications, 3 (2), [025003]. (doi:10.1088/2515-7620/abed94).

Record type: Article

Abstract

Deep learning has shown recent key breakthroughs in enabling particulate identification directly from scattering patterns. However, moving such a detector from a laboratory to a real-world environment means developing techniques for improving the neural network robustness. Here, a methodology for training data augmentation is proposed that is shown to ensure neural network accuracy, despite occlusion of the scattering pattern by simulated particulates deposited on the detector's imaging sensor surface. The augmentation approach was shown to increase the accuracy of the network when identifying the geometric Y-dimension of the particulates by ~62% when 1000 occlusions of size ~5 pixels were present on the scattering pattern. This capability demonstrates the potential of data augmentation for increasing accuracy and longevity of a particulate detector operating in a real-world environment.

Text
Pixel_JGJ_Final_Corrected - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (1MB)

More information

Accepted/In Press date: 10 March 2021
Published date: 17 March 2021
Additional Information: Funding Information: BM was supported by an EPSRC Early Career Fellowship (EP/N03368X/1) and EPSRC grant (EP/T026197/1). ML was supported by a BBSRC Future Leader Fellowship (BB/P011365/1) and a Senior Research Fellowship from the National Institute for Health Research Southampton Biomedical Research Centre. Publisher Copyright: © 2021 The Author(s). Published by IOP Publishing Ltd. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Deep learning, Optics, Particulate matter, Pollution, Sensing

Identifiers

Local EPrints ID: 448335
URI: http://eprints.soton.ac.uk/id/eprint/448335
ISSN: 2515-7620
PURE UUID: 5c27af85-f74c-438c-88f4-9add779aadbb
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for Matthew Loxham: ORCID iD orcid.org/0000-0001-6459-538X
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 20 Apr 2021 16:33
Last modified: 28 Apr 2022 02:11

Export record

Altmetrics

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×