Aedes aegypti Egg Counting with Neural Networks for Object Detection
This work addresses a domain-specific problem for public health and entomology researchers by automating egg counting, but appears incremental as it applies existing object detection methods to a new dataset.
The paper tackled the problem of automating egg counting for Aedes aegypti mosquitoes, a laborious task used in disease monitoring and research, by testing three neural networks (Faster R-CNN, Side-Aware Boundary Localization, and FoveaBox) on a new dataset of field and laboratory eggs, but did not report specific numerical results.
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.