Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency

• URL: http://arxiv.org/abs/2007.14863v4
• Date: Sat, 27 Nov 2021 20:51:04 GMT
• Title: Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency
• Authors: Wesley L. Passos, Gabriel M. Araujo, Amaro A. de Lima, Sergio L. Netto, and Eduardo A. B. da Silva
• Abstract summary: Aedes aegypti mosquito infects millions of people with diseases such as dengue zika, chikungunya, and urban yellow fever. Main form to combat these diseases is to avoid mosquito reproduction by searching for and eliminating the potential mosquito breeding grounds. In this work, we introduce a comprehensive dataset of aerial videos, acquired with an unmanned aerial vehicle, containing possible mosquito breeding sites.
• Score: 2.4858193569899907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Every year, the Aedes aegypti mosquito infects millions of people with diseases such as dengue, zika, chikungunya, and urban yellow fever. The main form to combat these diseases is to avoid mosquito reproduction by searching for and eliminating the potential mosquito breeding grounds. In this work, we introduce a comprehensive dataset of aerial videos, acquired with an unmanned aerial vehicle, containing possible mosquito breeding sites. All frames of the video dataset were manually annotated with bounding boxes identifying all objects of interest. This dataset was employed to develop an automatic detection system of such objects based on deep convolutional networks. We propose the exploitation of the temporal information contained in the videos by the incorporation, in the object detection pipeline, of a spatio-temporal consistency module that can register the detected objects, minimizing most false-positive and false-negative occurrences. Also, we experimentally show that using videos is more beneficial than only composing a mosaic using the frames. Using the ResNet-50-FPN as a backbone, we achieve F$_1$-scores of 0.65 and 0.77 on the object-level detection of `tires' and `water tanks', respectively, illustrating the system capabilities to properly locate potential mosquito breeding objects.

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