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.