Counting animals in aerial images with a density map estimation model

Qian, Yifei; Humphries, Grant R.W.; Trathan, Philip N. ORCID: https://orcid.org/0000-0001-6673-9930; Lowther, Andrew; Donovan, Carl R.. 2023 Counting animals in aerial images with a density map estimation model. Ecology and Evolution, 13 (4), e9903. 11, pp. https://doi.org/10.1002/ece3.9903

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Official URL: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9...

Abstract/Summary

Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual postprocessing 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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/ece3.9903
ISSN:	2041210X
Additional Keywords:	crowd-counting, machine-learning, image-processing, abundance estimation
Date made live:	11 Apr 2023 08:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533132

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/ece3.9903

ISSN:

2041210X

Additional Keywords:

crowd-counting, machine-learning, image-processing, abundance estimation

Date made live:

11 Apr 2023 08:29 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533132