Communication Dans Un Congrès Année : 2024

Aerosol characterization on noisy surfaces by deep-learning methods

Résumé

Experimental studies to characterize aerosol deposition in flow system (such as ventilation duct) may require avoiding a sampling substrate which modifies the flow in the boundary layer, influencing the deposition or the interaction forces. However, measuring the deposition of aerosols on surface without any intermediate substrate presents detection difficulties (Costa et al. 2021) which can be overcome by direct visualization of aerosols by microscopy, using the microscope as a scanner. However, analysing the deposition of aerosols on any surfaces by microscopy generates certain constraints regarding the use of known visualization techniques: inhomogeneous image backgrounds due to the surface structure, unsharp images due to the non-flatness of the real considered surfaces. Common image processing is then not very effective. The objective of this work is therefore to deploy advanced computer vision methods to process images acquired on typical surfaces of ventilation ducts (stainless steel). Little work exists to date on the detection of aerosols using neural networks. Monchot et al. (2021) were interested in nanoparticles, as were Merouane et al. (2023) but in these works, the particles stand out relatively well from the background.
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hal-04875162 , version 1 (08-01-2025)

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  • HAL Id : hal-04875162 , version 1

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Jeanne Malet, Philippe Querre, Simon Papine-Paktoris, Samy Blusseau, Santiago Velasco-Forero. Aerosol characterization on noisy surfaces by deep-learning methods. EAC2024 - The European Aerosol Conference, Aug 2024, Tampere, Finland. ⟨hal-04875162⟩
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