Aedes aegypti Egg Counting with Neural Networks for Object Detection
Micheli Nayara de Oliveira Vicente Gabriel Toshio Hirokawa Higa João Vitor de Andrade Porto Higor Henrique Picoli Nucci Asser Botelho Santana Karla Rejane de Andrade Porto Antonia Railda Roel Hemerson Pistori
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.