Animal Systematics, Evolution and Diversity

The Korean Society of Systematic Zoology (한국동물분류학회)
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Application of Deep Learning-Based Object Detection Models to Classify Images of Cacatua Parrot Species

  • Jung-Il Kim (Department of Biotechnology, Sangmyung University) ;
  • Jong-Won Baek (Department of Biotechnology, Sangmyung University) ;
  • Chang-Bae Kim (Department of Biotechnology, Sangmyung University)
  • Received : 2024.06.12
  • Accepted : 2024.09.25
  • Published : 2024.10.31
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Abstract

Parrots, especially the Cacatua species, are a particular focus for trade because of their mimicry, plumage, and intelligence. Indeed, Cacatua species are imported most into Korea. To manage trade in wildlife, it is essential to identify the traded species. This is conventionally achieved by morphological identification by experts, but the increasing volume of trade is overwhelming them. Identification of parrots, particularly Cacatua species, is difficult due to their similar features, leading to frequent misidentification. There is thus a need for tools to assist experts in accurately identifying Cacatua species in situ. Deep learning-based object detection models, such as the You Only Look Once (YOLO) series, have been successfully employed to classify wildlife and can help experts by reducing their workloads. Among these models, YOLO versions 5 and 8 have been widely applied for wildlife classification. The later model normally performs better, but selecting and designing a suitable model remains crucial for custom datasets, such as wildlife. Here, YOLO versions 5 and 8 were employed to classify 13 Cacatua species in the image data. Images of these species were collected from eBird, iNaturalist, and Google. The dataset was divided, with 80% used for training and validation and 20% for evaluating model performance. Model performance was measured by mean average precision, with YOLOv5 achieving 0.889 and YOLOv8 achieving 0.919. YOLOv8 was thus better than YOLOv5 at detecting and classifying Cacatua species in the examined images. The model developed here could significantly support the management of the global trade in Cacatua species.

Keywords

Acknowledgement

This work was supported by a grant from the National Institute of Biological Resources(NIBR), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIBRE 202411). We used the images from the Macaulay Library at the Cornell Lab of Ornithology during model training, and we thank the thousands of eBird participants and organizations for their contributions.

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