Aedes aegypti Egg Counting with Neural Networks for Object Detection

2024/03/12 by Micheli Nayara de Oliveira Vicente, Vicente, Micheli Nayara de Oliveira, Gabriel Toshio Hirokawa Higa +15
Agricultural and Biological Sciences · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Dengue and Mosquito Control Research #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Smart Agriculture and AI #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2403.08016

openalex publication_date 2024/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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