Aedes aegypti Egg Counting with Neural Networks for Object Detection

• Micheli Nayara de Oliveira Vicente UCDB
• João Vitor de Andrade Porto UCDB
• Gabriel Toshio Hirokawa Higa UCDB https://orcid.org/0000-0002-4766-3675
• Higor Henrique Picoli Nucci UFMS
• Asser Botelho Santana UCDB
• Karla Rejane de Andrade Porto Kerow Soluções de Precisão https://orcid.org/0000-0002-6309-8696
• Antonia Railda Roel UCDB https://orcid.org/0000-0002-6403-0554
• Hemerson Pistori UCDB

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 LIRAa and 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. With FoveaBox, we achieve a median mean absolute error of 6.854. Finally, we also discuss the main difficulties and possibilities for future research.

Keywords: Deep Learning, Ovitrap, Disease Vector Control, Counting