YOLOv7 for Mosquito Breeding Grounds Detection and Tracking
Camila Laranjeira Daniel Andrade Jefersson A. dos Santos
With the looming threat of climate change, neglected tropical diseases such
as dengue, zika, and chikungunya have the potential to become an even greater
global concern. Remote sensing technologies can aid in controlling the spread
of Aedes Aegypti, the transmission vector of such diseases, by automating the
detection and mapping of mosquito breeding sites, such that local entities can
properly intervene. In this work, we leverage YOLOv7, a state-of-the-art and
computationally efficient detection approach, to localize and track mosquito
foci in videos captured by unmanned aerial vehicles. We experiment on a dataset
released to the public as part of the ICIP 2023 grand challenge entitled
Automatic Detection of Mosquito Breeding Grounds. We show that YOLOv7 can be
directly applied to detect larger foci categories such as pools, tires, and
water tanks and that a cheap and straightforward aggregation of frame-by-frame
detection can incorporate time consistency into the tracking process.