Counting animals in aerial images with a density map estimation model

• Main Authors: Qian, Yifei, Humphries, Grant, Trathan, Philip, Lowther, Andrew, Donovan, Carl
• Format: Software
• Language: unknown
• Published: 2023
• Subjects: density estimation; drone imagery; Antarctic; Antarctic Peninsula; South Shetland Islands; The Antarctic; Antarc*; British Antarctic Survey
• Online Access: https://zenodo.org/record/7317264; https://doi.org/10.5281/zenodo.7317264

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 FundCrossref Funder Registry ID: http://dx.doi.org/10.13039/100001399Award Number: B095701 The British Antarctic Survey currently holds an archive of colour digital aerial photography from the Antarctic Peninsula and South Shetland Islands acquired between November and December 2013, and partially re-flown in November 2015. The archive contains images from approximately 140 Pygoscelis penguin colonies selected for a range of species, population sizes and topographic settings. The images were acquired using a large-format Intergraph DMC mapping camera, with a resolution of about 12 cm or better. The images each have a footprint of about 1600 m * 1000 m and were flown with 60% overlap to allow stereo-cover. For the images to be useful as part of an automated penguin counting process they needed significant pre-processing to geolocate them and remove terrain distortions inherent to the perspective view of a camera image. This processing comprised: 1) the stereo-images were used to extract a Digital ...