# YOLOv7 for Mosquito Breeding Grounds Detection and Tracking

arXiv: https://arxiv.org/abs/2310.10423 · HTML: https://mlsanity.com/paper/2310.10423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Seq-NMS for Video Object Detection](https://mlsanity.com/paper/1602.08465/) (2016)
- [YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss](https://mlsanity.com/paper/2204.06806/) (2022)
- [Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency](https://mlsanity.com/paper/2007.14863/) (2020)

## Built on by (most cited first, 1 of 1)

- [VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites](https://mlsanity.com/paper/2506.14629/) (2025)

## Abstract (arXiv)

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.