%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2019/10.02.13.22
%2 sid.inpe.br/sibgrapi/2019/10.02.13.22.14
%@doi 10.1109/SIBGRAPI.2019.00015
%T Acquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning
%D 2019
%A Garcia, Pedro Saint Clair,
%A Martins, Rafael,
%A Lins Machado, George Luiz,
%A Camara-Chavez, Guillermo,
%@affiliation Computer Science Department, Federal University of Ouro Preto
%@affiliation Biology Department, Federal University of Ouro Preto
%@affiliation Biology Department, Federal University of Ouro Preto
%@affiliation Computer Science Department, Federal University of Ouro Preto
%E Oliveira, Luciano Rebouças de,
%E Sarder, Pinaki,
%E Lage, Marcos,
%E Sadlo, Filip,
%B Conference on Graphics, Patterns and Images, 32 (SIBGRAPI)
%C Rio de Janeiro, RJ, Brazil
%8 28-31 Oct. 2019
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Aedes aegypti egg counting, mosquito eggs, deep learning.
%X 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 eggs from the others. In 10% of the test images, the count performed by the model, and the ground truth of the number of eggs was considered weakly correlated. This problem 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 the test images, the counting was considered to be perfectly correlated.
%@language en
%3 Paper ID 103.pdf