Particle Sensing via Deep Learning
Particle Sensing via Deep Learning
Airborne pollution, in the form of particles of sizes <10 μm (PM10) and <2.5 μm (PM2.5), is a global health challenge and linked to illnesses such as asthma, cancer, heart disease and dementia, contributing to around nine million deaths globally per year. Effective monitoring of airborne particles, particularly in residential areas, is urgently needed to better understand personal exposure and pollution hotspots. Whilst existing in-the-field sensors can provide an approximate count of the number of airborne particles, they are unable to identify properties such as the material type and shape of individual particles. The capability for parameter identification of individual particles is essential, as different pollution particle parameters are associated with specific health conditions. The interface of deep learning and imaging has seen extraordinary progress in the past few years, as computational power now enables image processing that can exceed human capability. The hypothesis is that deep learning will enable the creation of a low-cost, compact, real-time sensing device capable of simultaneous identification and imaging of individual airborne pollution particles. The focus of this talk will be on the application of convolutional neural networks for the identification of material type, shape and size of individual pollution particles directly from scattering patterns, as shown by the concept figure. Results for real-time sensing for both airborne and waterborne particles, and using both free-space lasers (including powered by a Raspberry Pi) and fibre optic bundles will be presented.
Mills, Benjamin
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Jain, Saurabh
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Xie, Yunhui
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MacKay, Benita Scout
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McDonnell, Michael David Tom
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Praeger, Matthew
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Richardson, David J.
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Eason, R.W.
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Grant-Jacob, James
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3 December 2020
Mills, Benjamin
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Jain, Saurabh
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Xie, Yunhui
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MacKay, Benita Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
McDonnell, Michael David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
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Richardson, David J.
ebfe1ff9-d0c2-4e52-b7ae-c1b13bccdef3
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Benjamin, Jain, Saurabh, Xie, Yunhui, MacKay, Benita Scout, McDonnell, Michael David Tom, Praeger, Matthew, Richardson, David J., Eason, R.W. and Grant-Jacob, James
(2020)
Particle Sensing via Deep Learning.
78th GASG Colloquium, “Airborne hazards: from gas to particles”.
03 Dec 2020.
Record type:
Conference or Workshop Item
(Other)
Abstract
Airborne pollution, in the form of particles of sizes <10 μm (PM10) and <2.5 μm (PM2.5), is a global health challenge and linked to illnesses such as asthma, cancer, heart disease and dementia, contributing to around nine million deaths globally per year. Effective monitoring of airborne particles, particularly in residential areas, is urgently needed to better understand personal exposure and pollution hotspots. Whilst existing in-the-field sensors can provide an approximate count of the number of airborne particles, they are unable to identify properties such as the material type and shape of individual particles. The capability for parameter identification of individual particles is essential, as different pollution particle parameters are associated with specific health conditions. The interface of deep learning and imaging has seen extraordinary progress in the past few years, as computational power now enables image processing that can exceed human capability. The hypothesis is that deep learning will enable the creation of a low-cost, compact, real-time sensing device capable of simultaneous identification and imaging of individual airborne pollution particles. The focus of this talk will be on the application of convolutional neural networks for the identification of material type, shape and size of individual pollution particles directly from scattering patterns, as shown by the concept figure. Results for real-time sensing for both airborne and waterborne particles, and using both free-space lasers (including powered by a Raspberry Pi) and fibre optic bundles will be presented.
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Published date: 3 December 2020
Venue - Dates:
78th GASG Colloquium, “Airborne hazards: from gas to particles”, 2020-12-03 - 2020-12-03
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Local EPrints ID: 472027
URI: http://eprints.soton.ac.uk/id/eprint/472027
PURE UUID: a1848f97-f7c0-4594-be0d-e96bf23f45b5
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Date deposited: 24 Nov 2022 17:30
Last modified: 21 Jun 2023 01:52
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Contributors
Author:
Saurabh Jain
Author:
Yunhui Xie
Author:
Benita Scout MacKay
Author:
Michael David Tom McDonnell
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