# Aedes aegypti Egg Counting with Neural Networks for Object Detection

Source: [Aedes aegypti Egg Counting with Neural Networks for Object Detection](<https://www.sota2.com/research/paper/68fa72831e209131cea56e8d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:27:53.383Z

Authors: Micheli Nayara de Oliveira Vicente, Gabriel Toshio Hirokawa Higa, Jo\\~ao Vitor de Andrade Porto, Higor Henrique, Picoli Nucci, Asser Botelho Santana, Karla Rejane de Andrade Porto, Antonia Railda Roel, Hemerson Pistori

Published: 2024-03-12T18:28:13.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.08016>)

[PDF](<https://arxiv.org/pdf/2403.08016.pdf>)