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Article

Implementation of an Award-Winning Invasive Fish Recognition and Separation System

1
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
2
School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou 510970, China
3
Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, USA
4
Smart Vision Works, Inc., Orem, UT 84058, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Gwanggil Jeon
Electronics 2021, 10(17), 2182; https://doi.org/10.3390/electronics10172182
Received: 15 August 2021 / Revised: 3 September 2021 / Accepted: 3 September 2021 / Published: 6 September 2021
(This article belongs to the Special Issue Convolutional Neural Networks and Vision Applications)
The state of Michigan, U.S.A., was awarded USD 1 million in March 2018 for the Great Lakes Invasive Carp Challenge. The challenge sought new and novel technologies to function independently of or in conjunction with those fish deterrents already in place to prevent the movement of invasive carp species into the Great Lakes from the Illinois River through the Chicago Area Waterway System (CAWS). Our team proposed an environmentally friendly, low-cost, vision-based fish recognition and separation system. The proposed solution won fourth place in the challenge out of 353 participants from 27 countries. The proposed solution includes an underwater imaging system that captures the fish images for processing, fish species recognition algorithm that identify invasive carp species, and a mechanical system that guides the fish movement and restrains invasive fish species for removal. We used our evolutionary learning-based algorithm to recognize fish species, which is considered the most challenging task of this solution. The algorithm was tested with a fish dataset consisted of four invasive and four non-invasive fish species. It achieved a remarkable 1.58% error rate, which is more than adequate for the proposed system, and required only a small number of images for training. This paper details the design of this unique solution and the implementation and testing that were accomplished since the challenge. View Full-Text
Keywords: computer vision; invasive carp; fish recognition; fish separation; evolutionary learning computer vision; invasive carp; fish recognition; fish separation; evolutionary learning
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MDPI and ACS Style

Chai, J.; Lee, D.-J.; Tippetts, B.; Lillywhite, K. Implementation of an Award-Winning Invasive Fish Recognition and Separation System. Electronics 2021, 10, 2182. https://doi.org/10.3390/electronics10172182

AMA Style

Chai J, Lee D-J, Tippetts B, Lillywhite K. Implementation of an Award-Winning Invasive Fish Recognition and Separation System. Electronics. 2021; 10(17):2182. https://doi.org/10.3390/electronics10172182

Chicago/Turabian Style

Chai, Jin, Dah-Jye Lee, Beau Tippetts, and Kirt Lillywhite. 2021. "Implementation of an Award-Winning Invasive Fish Recognition and Separation System" Electronics 10, no. 17: 2182. https://doi.org/10.3390/electronics10172182

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