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dc.contributor.authorQian, Yifei
dc.contributor.authorHumphries, Grant
dc.contributor.authorTrathan, Philip
dc.contributor.authorLowther, Andrew
dc.contributor.authorDonovan, Carl R.
dc.date.accessioned2023-04-10T09:30:02Z
dc.date.available2023-04-10T09:30:02Z
dc.date.issued2023-04-07
dc.identifier.citationQian , Y , Humphries , G , Trathan , P , Lowther , A & Donovan , C R 2023 , ' Counting animals in aerial images with a density map estimation model ' , Ecology and Evolution , vol. 13 , no. 4 , e9903 . https://doi.org/10.1002/ece3.9903en
dc.identifier.issn2045-7758
dc.identifier.otherPURE: 283563350
dc.identifier.otherPURE UUID: 40686cd1-95e5-494e-ba46-17e0e44182e3
dc.identifier.otherORCID: /0000-0002-1465-5193/work/133187145
dc.identifier.otherScopus: 85158994314
dc.identifier.urihttps://hdl.handle.net/10023/27367
dc.descriptionFunding: WWF (UK) supported PNT under grant GB095701, which also provided funds to develop the training datasets.en
dc.description.abstractAnimal 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.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofEcology and Evolutionen
dc.rightsCopyright © 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en
dc.subjectAbundance estimationen
dc.subjectDensity map estimationen
dc.subjectImage processingen
dc.subjectMachine learningen
dc.subjectQA Mathematicsen
dc.subjectDASen
dc.subjectMCCen
dc.subject.lccQAen
dc.titleCounting animals in aerial images with a density map estimation modelen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.identifier.doihttps://doi.org/10.1002/ece3.9903
dc.description.statusPeer revieweden


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