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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 34, Number 3(4) (2022)
Copyright(C) MYU K.K.
pp. 1229-1240
S&M2886 Research Paper of Special Issue
https://doi.org/10.18494/SAM3574
Published: March 24, 2022

Flexible IoT Cloud Application for Ornamental Fish Recognition Using YOLOv3 Model [PDF]

Chi-Tsai Yeh, Tzuo-Ming Chen, and Zhong-Jie Liu

(Received August 2, 2021; Accepted February 14, 2022)

Keywords: microservices, deep learning, cloud computing, Internet of Things, YOLOv3

The ornamental fish industry is a booming and emerging industry. Fish identification for fish farmers, trainers, sellers, and even buyers is an essential skill. With the rise of deep learning, object recognition is widely used in different fields. In this paper, we propose a highly flexible application via microservice architecture. It utilizes cloud computing to provide high-efficiency and low-cost identification services with NVIDIA T4 graphics processor units (GPUs) and introduces the You Only Look Once (YOLOv3) model to recognize the species of ornamental fish. Finally, mobile devices capture the photos and communicate with cloud through a representational state transfer (RESTful) application programming interface (API) to retrieve the predicted results. The proposed application identified 11 types of ornamental fish and completed each prediction within 1 s.

Corresponding author: Tzuo-Ming Chen


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Cite this article
Chi-Tsai Yeh, Tzuo-Ming Chen, and Zhong-Jie Liu, Flexible IoT Cloud Application for Ornamental Fish Recognition Using YOLOv3 Model, Sens. Mater., Vol. 34, No. 3, 2022, p. 1229-1240.



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