Counting animals in aerial images with a density map estimation model. Issue 4 (7th April 2023)

• Record Type: Journal Article
• Authors: Qian, Yifei; Humphries, Grant R. W.; Trathan, Philip N.; Lowther, Andrew; Donovan, Carl R.
• Is Part Of: Ecology and evolution. Volume 13:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-07
• Subjects: abundance estimation -- density map estimation -- image processing -- machine learning; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.9903
• Languages: English
• ISSNs: 2045-7758

Abstract

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. Abstract : We present here an adaptation of state‐of‐the‐art crowd‐counting methodologies for counting of penguins (average size is about 5 * 5 pixels) from aerial photography. Our method performed significantly better in terms of model performance and computational efficiency than standard Faster‐RCNN deep learning approaches and gave an error rate of only 0.8 percent.