1. Animal abundance estimation is increasingly based on drone or aerial
survey photography. Manual post-processing has been used extensively,
however volumes of such data are increasing, necessitating some level of
automation, either for complete counting, or as a labour-saving tool.
Any automated processing can be challenging when using the tools on
species that nest in close formation such as Pygoscelid penguins. 2. We
present here an adaptation of state-of-the-art crowd-counting
methodologies for counting of penguins from aerial photography. 3. The
crowd-counting model performed significantly better in terms of model
performance and computational efficiency than standard Faster RCNN
deep-learning approaches and gave an error rate of only 0.8 percent. 4.
Crowd-counting techniques as demonstrated here have the ability to
vastly improve our ability to count animals in tight aggregations, which
will demonstrably improve monitoring efforts from aerial imagery.