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Diffuse retro-reflective imaging for improved video tracking of mosquitoes at human baited bednets

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Voloshin, Vitaly, Kröner, Christian, Seniya, Chandrabhan, Murray, Gregory, Guy, Amy, Towers, Catherine E., McCall, Philip ORCID: https://orcid.org/0000-0002-0007-3985 and Towers, David P. (2020) 'Diffuse retro-reflective imaging for improved video tracking of mosquitoes at human baited bednets'. The Royal Society Open Science, Vol 7, Issue 191951.

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Abstract

Robust imaging techniques for tracking insects have been essential tools in numerous laboratory and field studies on pests, beneficial insects and model systems. Recent innovations in optical imaging systems and associated signal processing have enabled detailed characterization of nocturnal mosquito behaviour around bednets and improvements in bednet design, a global essential for protecting populations against malaria. Nonetheless, there remain challenges around ease of use for large-scale in situ recordings and extracting data reliably in the critical areas of the bednet where the optical signal is attenuated. Here, we introduce a retro-reflective screen at the back of the measurement volume, which can simultaneously provide diffuse illumination, and remove optical alignment issues while requiring only one-sided access to the measurement space. The illumination becomes significantly more uniform, although noise removal algorithms are needed to reduce the effects of shot noise, particularly across low-intensity bednet regions. By systematically introducing mosquitoes in front of and behind the bednet in laboratory experiments, we are able to demonstrate robust tracking in these challenging areas. Overall, the retro-reflective imaging set-up delivers mosquito segmentation rates in excess of 90% compared to less than 70% with backlit systems.

Item Type: Article
Subjects: QX Parasitology > Insects. Other Parasites > QX 510 Mosquitoes
WA Public Health > Preventive Medicine > WA 110 Prevention and control of communicable diseases. Transmission of infectious diseases
Faculty: Department: Biological Sciences > Vector Biology Department
Digital Object Identifer (DOI): https://doi.org/10.1098/rsos.191951
Depositing User: Mel Finley
Date Deposited: 14 May 2020 10:36
Last Modified: 14 May 2020 10:36
URI: https://archive.lstmed.ac.uk/id/eprint/14457

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