  Monitoring of colonial waterbird nesting islands is essential to tracking
waterbird population trends, which are used for evaluating ecosystem health and
informing conservation management decisions. Recently, unmanned aerial
vehicles, or drones, have emerged as a viable technology to precisely monitor
waterbird colonies. However, manually counting waterbirds from hundreds, or
potentially thousands, of aerial images is both difficult and time-consuming.
In this work, we present a deep learning pipeline that can be used to precisely
detect, count, and monitor waterbirds using aerial imagery collected by a
commercial drone. By utilizing convolutional neural network-based object
detectors, we show that we can detect 16 classes of waterbird species that are
commonly found in colonial nesting islands along the Texas coast. Our
experiments using Faster R-CNN and RetinaNet object detectors give mean
interpolated average precision scores of 67.9% and 63.1% respectively.
