Counting animals in aerial images with a density map estimation model

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 such tools on species
that nest in close formation such as Pygoscelis penguins. We present here
a customized CNN-based density map estimation method for counting of
penguins from low-resolution aerial photography. Our model, an indirect
regression algorithm, performed significantly better in terms of counting
accuracy than standard detection algorithm (Faster RCNN) when counting
small objects from low-resolution images and gave an error rate of only
0.8 percent. Density map estimation methods as demonstrated here can
vastly improve our ability to count animals in tight aggregations, and
demonstrably improve monitoring efforts from aerial imagery.