Drones and deep learning produce accurate and efficient monitoring of large-scale seabird colonies

Abstract

Abstract Population monitoring of colonial seabirds is often complicated by the large size of colonies, remote locations, and close inter- and intra-species aggregation. While drones have been successfully used to monitor large inaccessible colonies, the vast amount of imagery collected introduces a data analysis bottleneck. Convolutional neural networks (CNN) are evolving as a prominent means for object detection and can be applied to drone imagery for population monitoring. In this study, we explored the use of these technologies to increase capabilities for seabird monitoring by using CNNs to detect and enumerate Black-browed Albatrosses (Thalassarche melanophris) and Southern Rockhopper Penguins (Eudyptes c. chrysocome) at one of their largest breeding colonies, the Falkland (Malvinas) Islands. Our results showed that these techniques have great potential for seabird monitoring at significant and spatially complex colonies, producing accuracies of correctly detecting and counting birds at 97.66% (Black-browed Albatrosses) and 87.16% (Southern Rockhopper Penguins), with 90% of automated counts being within 5% of manual counts from imagery. The results of this study indicate CNN methods are a viable population assessment tool, providing opportunities to reduce manual labor, cost, and human error.