Deep object detection for waterbird monitoring using aerial imagery

TL;DR

This paper introduces a deep learning pipeline utilizing convolutional neural networks to accurately detect and count 16 waterbird species from aerial drone imagery, aiding conservation efforts.

Contribution

It presents a novel application of Faster R-CNN and RetinaNet detectors for waterbird monitoring with high precision from aerial images.

Findings

Faster R-CNN achieved 67.9% mean interpolated average precision.

RetinaNet achieved 63.1% mean interpolated average precision.

The pipeline enables efficient, automated waterbird detection from drone imagery.

Abstract

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.