Young Researcher Paper Award 2023
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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.
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
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 5(1) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1613-1617
S&M3274 Letter of Special Issue https://doi.org/10.18494/SAM4077 Published: May 12, 2023 Approaches to Upgrading the Performance of Fishing Vessel Recognition Technology [PDF] Ching-Hai Lin, Chun-Cheng Lin, Yu-Cheng Zhan, Cheng-Yu Yeh, and Shaw-Hwa Hwang (Received August 5, 2022; Accepted April 13, 2023) Keywords: fishing vessel identification, image recognition, deep learning, ArcFace
Fishing vessel recognition using face recognition has recently been addressed for the first time. This paper is actually an improved version of the original proposal, and there are two steps to improve the performance of fishing vessel recognition. In the first step, the number of recognizable fishing vessels was increased considerably from 156 to 272 and the numbers of images of different vessels were made as uniform as possible for a higher generalization ability. In the second step, an EfficientNet model was employed, input images were resized to 480 × 160 pixels to undistortedly display the side views of fishing vessels, and finally, the ArcFace loss function was used as well to train the presented model. As it turned out, the overall recognition performance was improved.
Corresponding author: Cheng-Yu Yeh  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Ching-Hai Lin, Chun-Cheng Lin, Yu-Cheng Zhan, Cheng-Yu Yeh, and Shaw-Hwa Hwang, Approaches to Upgrading the Performance of Fishing Vessel Recognition Technology, Sens. Mater., Vol. 35, No. 5, 2023, p. 1613-1617. |
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