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.

翻译：随着气候变化威胁的加剧，登革热、寨卡病毒和基孔肯雅热等被忽视的热带疾病可能成为更严峻的全球性问题。遥感技术可通过自动化检测与绘制蚊虫孳生地地图，协助控制此类疾病的传播媒介——埃及伊蚊的扩散，使地方机构能够及时采取干预措施。本研究利用YOLOv7这一先进且计算高效的检测方法，对无人机拍摄视频中的蚊虫集中区域进行定位与跟踪。实验基于ICIP 2023大挑战赛"蚊虫孳生地自动检测"公开数据集展开。研究表明，YOLOv7可直接用于检测池塘、轮胎、水箱等较大类别的孳生聚集地，而通过简单廉价的逐帧检测聚合方法，可在跟踪过程中引入时间一致性机制。