Nouman Javed, Adam J López-Denman, Prasad N Paradkar, Asim Bhatti
{"title":"FlightTrackAI:基于卷积神经网络的强健工具,用于追踪埃及伊蚊的飞行行为。","authors":"Nouman Javed, Adam J López-Denman, Prasad N Paradkar, Asim Bhatti","doi":"10.1098/rsos.240923","DOIUrl":null,"url":null,"abstract":"<p><p>Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.</p>","PeriodicalId":21525,"journal":{"name":"Royal Society Open Science","volume":"11 10","pages":"240923"},"PeriodicalIF":2.9000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11444788/pdf/","citationCount":"0","resultStr":"{\"title\":\"FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour of <i>Aedes aegypti</i> mosquitoes.\",\"authors\":\"Nouman Javed, Adam J López-Denman, Prasad N Paradkar, Asim Bhatti\",\"doi\":\"10.1098/rsos.240923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.</p>\",\"PeriodicalId\":21525,\"journal\":{\"name\":\"Royal Society Open Science\",\"volume\":\"11 10\",\"pages\":\"240923\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11444788/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Royal Society Open Science\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1098/rsos.240923\",\"RegionNum\":3,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Royal Society Open Science","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1098/rsos.240923","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour of Aedes aegypti mosquitoes.
Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.
期刊介绍:
Royal Society Open Science is a new open journal publishing high-quality original research across the entire range of science on the basis of objective peer-review.
The journal covers the entire range of science and mathematics and will allow the Society to publish all the high-quality work it receives without the usual restrictions on scope, length or impact.