Acquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning. - Garcia, Pedro Saint; Martins, Rafael; Coelho, George; Camara-Chavez, Guillermo (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2019: Anais da XXXII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.2019.9777, 2019)
The mosquito Aedes aegypti can transmit some diseases, which makes the study of the proliferation of this vector a necessary task. With the use of traps made in the laboratory, called ovitraps, it is possible to map egg deposition in a community. Through a camera, coupled with a magnifying glass are acquired images containing the elements (eggs) to be counted. First, the goal is to find pixels with a similar color to mosquito eggs, for that, we take advantage of the slice color method. From these already worked images, a process of transfer learning with a convolutional neural network (CNN) is carried out. The intention is to separate which elements are actually eggs from the others. In 10% of the test images, the count performed by the model in relation to the actual number of eggs was considered to be weakly correlated, this occurs in images that have a high density of eggs or appear black elements that resemble mosquito eggs, but they are not. For the remaining 90% of test images, the counting was considered to be perfectly correlated.