  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.
