Object recognition algorithm for the automatic identification and removal of invasive fish

Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing th...

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Vydáno v:	Biosystems engineering Ročník 145; s. 65 - 75
Hlavní autoři:	Zhang, Dong, Lee, Dah-Jye, Zhang, Meng, Tippetts, Beau J., Lillywhite, Kirt D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.05.2016
Témata:	Algorithms Automation biodiversity Classification Computer vision data collection ECO-Features Economics ecosystems experts financial economics Fish Fish species recognition humans indigenous species invasive species Invasive species monitoring monitoring Monitors Object recognition Standard deviation statistical analysis Computer vision Invasive species monitoring Fish species recognition ECO-Features
ISSN:	1537-5110, 1537-5129
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Abstract	Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing them is the best way to reduce the long-term cost of eradication or control. This paper proposes an object recognition algorithm to automatically identify fish species. Our previous work on general object recognition, called Evolution-COnstructed (ECO) Features, was modified and adapted to construct features and use AdaBoost to classify different fish species. The proposed algorithm does not depend on human experts to design features for fish species classification, but constructs efficient features automatically. Results from experiments show the proposed method obtained an average of 98.9% classification accuracy with a standard deviation of 0.96% with a dataset composed of 8 fish species and a total of 1049 images. Using this algorithm, a fish monitoring system can be built to remove invasive species and monitor native fish abundance, distribution, and size with minimal collateral impact and fish suffering. [Display omitted] •We discuss the importance of invasive fish species removal.•We propose to use Evolution COnstructed (ECO) Features for fish species recognition.•We discuss the uniqueness and advantages of ECO-Features.•Eight fish species including invasive and non-invasive are included for experiments.
AbstractList	Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing them is the best way to reduce the long-term cost of eradication or control. This paper proposes an object recognition algorithm to automatically identify fish species. Our previous work on general object recognition, called Evolution-COnstructed (ECO) Features, was modified and adapted to construct features and use AdaBoost to classify different fish species. The proposed algorithm does not depend on human experts to design features for fish species classification, but constructs efficient features automatically. Results from experiments show the proposed method obtained an average of 98.9% classification accuracy with a standard deviation of 0.96% with a dataset composed of 8 fish species and a total of 1049 images. Using this algorithm, a fish monitoring system can be built to remove invasive species and monitor native fish abundance, distribution, and size with minimal collateral impact and fish suffering. [Display omitted] •We discuss the importance of invasive fish species removal.•We propose to use Evolution COnstructed (ECO) Features for fish species recognition.•We discuss the uniqueness and advantages of ECO-Features.•Eight fish species including invasive and non-invasive are included for experiments. Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing them is the best way to reduce the long-term cost of eradication or control. This paper proposes an object recognition algorithm to automatically identify fish species. Our previous work on general object recognition, called Evolution-COnstructed (ECO) Features, was modified and adapted to construct features and use AdaBoost to classify different fish species. The proposed algorithm does not depend on human experts to design features for fish species classification, but constructs efficient features automatically. Results from experiments show the proposed method obtained an average of 98.9% classification accuracy with a standard deviation of 0.96% with a dataset composed of 8 fish species and a total of 1049 images. Using this algorithm, a fish monitoring system can be built to remove invasive species and monitor native fish abundance, distribution, and size with minimal collateral impact and fish suffering.
Author	Zhang, Dong Lee, Dah-Jye Zhang, Meng Lillywhite, Kirt D. Tippetts, Beau J.
Author_xml	– sequence: 1 givenname: Dong surname: Zhang fullname: Zhang, Dong organization: School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China – sequence: 2 givenname: Dah-Jye orcidid: 0000-0003-1752-8146 surname: Lee fullname: Lee, Dah-Jye email: djlee@byu.edu organization: Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, USA – sequence: 3 givenname: Meng surname: Zhang fullname: Zhang, Meng organization: Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, USA – sequence: 4 givenname: Beau J. surname: Tippetts fullname: Tippetts, Beau J. organization: Smart Vision Works International, LLC., Orem, UT, USA – sequence: 5 givenname: Kirt D. surname: Lillywhite fullname: Lillywhite, Kirt D. organization: Smart Vision Works International, LLC., Orem, UT, USA
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SubjectTerms	Algorithms Automation biodiversity Classification Computer vision data collection ECO-Features Economics ecosystems experts financial economics Fish Fish species recognition humans indigenous species invasive species Invasive species monitoring monitoring Monitors Object recognition Standard deviation statistical analysis
Title	Object recognition algorithm for the automatic identification and removal of invasive fish
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