Counting animals in aerial images with a density map estimation model

Yifei Qian*, Grant Humphries, Philip Trathan, Andrew Lowther, Carl R. Donovan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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.
Original languageEnglish
Article numbere9903
Number of pages11
JournalEcology and Evolution
Volume13
Issue number4
DOIs
Publication statusPublished - 7 Apr 2023

Keywords

  • Abundance estimation
  • Density map estimation
  • Image processing
  • Machine learning

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