A YOLO-Based Approach for Aedes Aegypti Larvae Classification and Detection

Mosquitoes are small, flying insects that can be found almost anywhere in the world. Although most of these creatures are just considered a nuisance, a few of its species can be vectors of some of the deadliest viruses and diseases that can greatly affect living organisms-animals and people alike. Aedes aegypti species, for instance, are deemed as the primary vector of the infamous dengue virus that has been around for a long time. With this, there have been many studies, mostly only with limited success, in different fields of learning on how to control and suppress both the mosquitoes and the diseases they carry. While some of these studies and research experiments are geared towards the use of modern technology such as machine learning, deep learning, and image processing, there are still a lot of strategies and methods that are yet to be discovered to improve present-day approaches and attain better results that are more sustainable and efficient. And so, the main goal of this study is to aid in the early suppression and prevention of the primary vector of dengue by creating a system implemented with the You Only Look Once (YOLO) algorithm that can detect and classify whether a mosquito larva is an Aedes aegypti species or not. Results showed that the system was able to correctly recognize most of the larval samples subjected to testing, with a root mean square error of 0.45 and 0.77 for Aedes and Non-Aedes larvae respectively.