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

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.

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Identifiers

ORCID for James Grant-Jacob: orcid.org/0000-0002-4270-4247

ORCID for Matthew Praeger: orcid.org/0000-0002-5814-6155

ORCID for Matthew Loxham: orcid.org/0000-0001-6459-538X

ORCID for R.W. Eason: orcid.org/0000-0001-9704-2204

ORCID for Benjamin Mills: orcid.org/0000-0002-1784-1012

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