FlightTrackAI: a convolutional neural network-based software for tracking the flight behaviour of Aedes aegypti mosquitoes

Abstract

Abstract 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 novel convolutional neural network (CNN)-based software for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and cubic spline 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% and an average mean absolute error of 0.23 pixels. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including diurnal and nocturnal locomotor activity, host-seeking behaviour, flight distance, volume coverage during flights, and speed measurement. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.