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Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network
Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosqu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359002/ https://www.ncbi.nlm.nih.gov/pubmed/37471341 http://dx.doi.org/10.1371/journal.pone.0284819 |
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author | Javed, Nouman Paradkar, Prasad N. Bhatti, Asim |
author_facet | Javed, Nouman Paradkar, Prasad N. Bhatti, Asim |
author_sort | Javed, Nouman |
collection | PubMed |
description | Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosquito traps. However, it is difficult to understand the flight behaviour of mosquitoes due to their small sizes, complicated poses, and seemingly random moving patterns. Currently, no open-source tool is available that can detect and track resting or flying mosquitoes. Our work presented in this paper provides a detection and trajectory estimation method using the Mask RCNN algorithm and spline interpolation, which can efficiently detect mosquitoes and track their trajectories with higher accuracy. The method does not require special equipment and works excellently even with low-resolution videos. Considering the mosquito size, the proposed method’s detection performance is validated using a tracker error and a custom metric that considers the mean distance between positions (estimated and ground truth), pooled standard deviation, and average accuracy. The results showed that the proposed method could successfully detect and track the flying (≈ 96% accuracy) as well as resting (100% accuracy) mosquitoes. The performance can be impacted in the case of occlusions and background clutters. Overall, this research serves as an efficient open-source tool to facilitate further examination of mosquito behavioural traits. |
format | Online Article Text |
id | pubmed-10359002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103590022023-07-21 Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network Javed, Nouman Paradkar, Prasad N. Bhatti, Asim PLoS One Research Article Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosquito traps. However, it is difficult to understand the flight behaviour of mosquitoes due to their small sizes, complicated poses, and seemingly random moving patterns. Currently, no open-source tool is available that can detect and track resting or flying mosquitoes. Our work presented in this paper provides a detection and trajectory estimation method using the Mask RCNN algorithm and spline interpolation, which can efficiently detect mosquitoes and track their trajectories with higher accuracy. The method does not require special equipment and works excellently even with low-resolution videos. Considering the mosquito size, the proposed method’s detection performance is validated using a tracker error and a custom metric that considers the mean distance between positions (estimated and ground truth), pooled standard deviation, and average accuracy. The results showed that the proposed method could successfully detect and track the flying (≈ 96% accuracy) as well as resting (100% accuracy) mosquitoes. The performance can be impacted in the case of occlusions and background clutters. Overall, this research serves as an efficient open-source tool to facilitate further examination of mosquito behavioural traits. Public Library of Science 2023-07-20 /pmc/articles/PMC10359002/ /pubmed/37471341 http://dx.doi.org/10.1371/journal.pone.0284819 Text en © 2023 Javed et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Javed, Nouman Paradkar, Prasad N. Bhatti, Asim Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
title | Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
title_full | Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
title_fullStr | Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
title_full_unstemmed | Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
title_short | Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
title_sort | flight behaviour monitoring and quantification of aedes aegypti using convolution neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359002/ https://www.ncbi.nlm.nih.gov/pubmed/37471341 http://dx.doi.org/10.1371/journal.pone.0284819 |
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