Dataset Open Access
Qian, Yifei; Humphries, Grant; Trathan, Philip; Lowther, Andrew; Donovan, Carl
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.
Funding provided by: World Wildlife Fund
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100001399
Award Number: B095701
|Data volume||1.2 GB|