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。

IF 2.9 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Royal Society Open Science Pub Date : 2024-10-02 eCollection Date: 2024-10-01 DOI:10.1098/rsos.240923
Nouman Javed, Adam J López-Denman, Prasad N Paradkar, Asim Bhatti
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引用次数: 0

摘要

监测蚊子的飞行行为对于评估它们的体能水平和了解它们在疾病传播中的潜在作用至关重要。现有的追踪蚊子飞行行为的方法在实验室环境中实施具有挑战性,而且它们在身份追踪方面也很困难,特别是在遮挡期间。在此,我们介绍基于卷积神经网络(CNN)的蚊子飞行自动跟踪工具 FlightTrackAI。FlightTrackAI 采用 CNN、多目标跟踪算法和插值法来跟踪飞行行为。它能在没有监督的情况下自动处理输入文件夹中的每段视频,并生成带有蚊子在各帧中位置的跟踪视频以及插值前后的轨迹图。FlightTrackAI 不需要复杂的设置来捕捉视频;使用标准实验室笼子录制的视频也能表现出色。FlightTrackAI 还提供过滤功能,以消除反射等短时物体。FlightTrackAI 的验证结果表明其性能卓越,平均准确率高达 99.9%。遮挡后正确分配身份的比例超过 91%。FlightTrackAI 生成的数据有助于分析各种与飞行相关的行为,包括飞行距离和飞行过程中的体积覆盖范围。这一进步有助于加深我们对蚊子生态和行为的了解,从而为有针对性的病媒控制策略提供信息。
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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.

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来源期刊
Royal Society Open Science
Royal Society Open Science Multidisciplinary-Multidisciplinary
CiteScore
6.00
自引率
0.00%
发文量
508
审稿时长
14 weeks
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.
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