YOLOv7 for Mosquito Breeding Grounds Detection and Tracking

• URL: http://arxiv.org/abs/2310.10423v1
• Date: Mon, 16 Oct 2023 14:04:25 GMT
• Title: YOLOv7 for Mosquito Breeding Grounds Detection and Tracking
• Authors: Camila Laranjeira and Daniel Andrade and Jefersson A. dos Santos
• Abstract summary: We leverage YOLOv7 to localize and track mosquito change in state videos captured by unmanned aerial vehicles. We show that YOLOv7 can be directly applied to detect larger categories such as tires and water tanks.
• Score: 5.729171000212229
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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