| Texto completo | |
| Autor(es): |
Bravo, Daniel Trevisan
[1]
;
Lima, Gustavo Araujo
[1]
;
Luz Alves, Wonder Alexandre
[1]
;
Colombo, Vitor Pessoa
[2]
;
Djogbenou, Luc
[3]
;
Denser Pamboukian, Sergio Vicente
[4]
;
Quaresma, Cristiano Capellani
[5]
;
de Araujo, Sidnei Alves
[1]
Número total de Autores: 8
|
| Afiliação do(s) autor(es): | [1] Nove Julho Univ, Informat & Knowledge Management Postgrad Program, Vergueiro St 235-249, BR-235249 Sao Paulo, SP - Brazil
[2] Ecole Polytech Fed Lausanne, Communaute Etud Amenagement Terr CEAT, Batiment BP Stn 16, CH-1015 Lausanne, VD - Switzerland
[3] Univ Abomey Calavi UAC, Ctr Rech Lutte Malad Infect CReMIT, Campus Abomey Calavi BP 526, Cotonou - Benin
[4] Univ Prebiteriana Mackenzie, Sci & Geospatial Applicat Postgrad Program, Consolacao St, 896 Bldg 45, 7th Floor Consolacao, Sao Paulo, SP - Brazil
[5] Nove Julho Univ, Smart & Sustainable Cities Postgrad Program, Vergueiro St 235-249, BR-235249 Sao Paulo, SP - Brazil
Número total de Afiliações: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS; v. 90, NOV 2021. |
| Citações Web of Science: | 0 |
| Resumo | |
The World Health Organization (WHO) has stated that effective vector control measures are critical to achieving and sustaining reduction of vector-borne infectious disease incidence. Unmanned aerial vehicles (UAVs), popularly known as drones, can be an important technological tool for health surveillance teams to locate and eliminate mosquito breeding sites in areas where vector-borne diseases such as dengue, zika, chikungunya or malaria are endemic, since they allow the acquisition of aerial images with high spatial and temporal resolution. Currently, though, such images are often analyzed through manual processes that are excessively time-consuming when implementing vector control interventions. In this work we propose computational approaches for the automatic identification of objects and scenarios suspected of being potential mosquito breeding sites from aerial images acquired by drones. These approaches were developed using convolutional neural networks (CNN) and Bag of Visual Words combined with the Support Vector Machine classifier (BoVW + SVM), and their performances were evaluated in terms of mean Average Precision - mAP-50. In the detection of objects using a CNN YOLOv3 model the rate of 0.9651 was obtained for the mAP-50. In the detection of scenarios, in which the performances of BoVW+SVM and a CNN YOLOv3 were compared, the respective rates of 0.6453 and 0.9028 were obtained. These findings indicate that the proposed CNN-based approaches can be used to identify potential mosquito breeding sites from images acquired by UAVs, providing substantial improvements in vector control programs aiming the reduction of mosquito-breeding sources in the environment. (AU) | |
| Processo FAPESP: | 19/05748-0 - Abordagem computacional para identificação automática de possíveis focos do mosquito Aedes aegypti a partir de imagens aéreas adquiridas por VANTs |
| Beneficiário: | Sidnei Alves de Araújo |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |