YOLOv7 for Mosquito Breeding Grounds Detection and Tracking
This work addresses the detection of mosquito breeding sites for public health entities, but it is incremental as it applies an existing method to a new dataset with minor adaptations.
The paper tackled the problem of detecting and tracking mosquito breeding grounds from UAV videos to aid in controlling disease spread, showing that YOLOv7 can directly detect larger foci categories like pools, tires, and water tanks with a straightforward aggregation method for time-consistent tracking.
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.