Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency
This addresses a public health issue by improving mosquito control efforts, but it is incremental as it builds on existing object detection methods with a novel module for video data.
The paper tackles the problem of automatically detecting Aedes aegypti mosquito breeding grounds from aerial videos to combat diseases like dengue, achieving F1-scores of 0.65 for tires and 0.77 for water tanks using a deep network with spatio-temporal consistency.
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.