Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance

Owing to climate change and human indiscriminate development, the population of endangered species has been decreasing. To protect endangered species, many countries worldwide have adopted the CITES treaty to prevent the extinction of endangered plants and animals. Moreover, research has been conduc...

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Autores principales:	Woohyuk Jang, Eui Chul Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	object detection deep neural network parrot classification CITES illegal transaction Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f1702ca2f8b040d0950a380aa7406d59
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spelling	oai:doaj.org-article:f1702ca2f8b040d0950a380aa7406d592021-11-25T16:47:24ZMulti-Class Parrot Image Classification Including Subspecies with Similar Appearance10.3390/biology101111402079-7737https://doaj.org/article/f1702ca2f8b040d0950a380aa7406d592021-11-01T00:00:00Zhttps://www.mdpi.com/2079-7737/10/11/1140https://doaj.org/toc/2079-7737Owing to climate change and human indiscriminate development, the population of endangered species has been decreasing. To protect endangered species, many countries worldwide have adopted the CITES treaty to prevent the extinction of endangered plants and animals. Moreover, research has been conducted using diverse approaches, particularly deep learning-based animal and plant image recognition methods. In this paper, we propose an automated image classification method for 11 endangered parrot species included in CITES. The 11 species include subspecies that are very similar in appearance. Data images were collected from the Internet and built in cooperation with Seoul Grand Park Zoo to build an indigenous database. The dataset for deep learning training consisted of 70% training set, 15% validation set, and 15% test set. In addition, a data augmentation technique was applied to reduce the data collection limit and prevent overfitting. The performance of various backbone CNN architectures (i.e., VGGNet, ResNet, and DenseNet) were compared using the SSD model. The experiment derived the test set image performance for the training model, and the results show that the DenseNet18 had the best performance with an mAP of approximately 96.6% and an inference time of 0.38 s.Woohyuk JangEui Chul LeeMDPI AGarticleobject detectiondeep neural networkparrot classificationCITESillegal transactionBiology (General)QH301-705.5ENBiology, Vol 10, Iss 1140, p 1140 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	object detection deep neural network parrot classification CITES illegal transaction Biology (General) QH301-705.5
spellingShingle	object detection deep neural network parrot classification CITES illegal transaction Biology (General) QH301-705.5 Woohyuk Jang Eui Chul Lee Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
description	Owing to climate change and human indiscriminate development, the population of endangered species has been decreasing. To protect endangered species, many countries worldwide have adopted the CITES treaty to prevent the extinction of endangered plants and animals. Moreover, research has been conducted using diverse approaches, particularly deep learning-based animal and plant image recognition methods. In this paper, we propose an automated image classification method for 11 endangered parrot species included in CITES. The 11 species include subspecies that are very similar in appearance. Data images were collected from the Internet and built in cooperation with Seoul Grand Park Zoo to build an indigenous database. The dataset for deep learning training consisted of 70% training set, 15% validation set, and 15% test set. In addition, a data augmentation technique was applied to reduce the data collection limit and prevent overfitting. The performance of various backbone CNN architectures (i.e., VGGNet, ResNet, and DenseNet) were compared using the SSD model. The experiment derived the test set image performance for the training model, and the results show that the DenseNet18 had the best performance with an mAP of approximately 96.6% and an inference time of 0.38 s.
format	article
author	Woohyuk Jang Eui Chul Lee
author_facet	Woohyuk Jang Eui Chul Lee
author_sort	Woohyuk Jang
title	Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
title_short	Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
title_full	Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
title_fullStr	Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
title_full_unstemmed	Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance
title_sort	multi-class parrot image classification including subspecies with similar appearance
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f1702ca2f8b040d0950a380aa7406d59
work_keys_str_mv	AT woohyukjang multiclassparrotimageclassificationincludingsubspecieswithsimilarappearance AT euichullee multiclassparrotimageclassificationincludingsubspecieswithsimilarappearance
_version_	1718412992602701824

Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance

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