Aedes aegypti is still one of the main concerns when it comes to disease vectors. Among the many ways to deal with it, there are important protocols that make use of egg numbers in ovitraps to calculate indices, such as the LIRAa and the Breteau Index, which can provide information on predictable outbursts and epidemics. Also, there are many research lines that require egg numbers, specially when mass production of mosquitoes is needed. Egg counting is a laborious and error-prone task that can be automated via computer vision-based techniques, specially deep learning-based counting with object detection. In this work, we propose a new dataset comprising field and laboratory eggs, along with test results of three neural networks applied to the task: Faster R-CNN, Side-Aware Boundary Localization and FoveaBox.
Cite
@article{arxiv.2403.08016,
title = {Aedes aegypti Egg Counting with Neural Networks for Object Detection},
author = {Micheli Nayara de Oliveira Vicente and Gabriel Toshio Hirokawa Higa and João Vitor de Andrade Porto and Higor Henrique and Picoli Nucci and Asser Botelho Santana and Karla Rejane de Andrade Porto and Antonia Railda Roel and Hemerson Pistori},
journal= {arXiv preprint arXiv:2403.08016},
year = {2024}
}