Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles

Data de publicação
Computers, Environment and Urban Systems
Citações (Scopus)
Bravo D.T.
Lima G.A.
Alves W.A.L.
Colombo V.P.
Djogbenou L.
Pamboukian S.V.D.
Quaresma C.C.
Araujo S.A.D.
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© 2021 Elsevier LtdThe 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.
Assuntos Scopus
Automatic Detection , Bag-of-visual-words , Computational approach , Health surveillances , Spatial and temporal resolutions , Support vector machine classifiers , Vector-borne disease , World Health Organization