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 10(2) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4653-4669
S&M3420 Related Technologies https://doi.org/10.18494/SAM4589 Published: October 24, 2023 An Improved Faster Region-based Convolutional Neural Network Algorithm for Identification of Steel Coil End-head [PDF] Jian-Zhou Pan, Chi-Hsin Yang, Long Wu, Wen-Hu Tang, and Kung-Chieh Wang (Received July 14, 2023; Accepted October 5, 2023) Keywords: steel coil end-head, improved faster region-based convolutional neural network (F-RCNN) algorithm, deep learning, feature pyramid network (FPN), parallel attention module (PAM)
A method that uses machine vision and machine learning technologies to identify the end-head in a steel coil has seldom been proposed. In this study, an improved faster region-based convolutional neural network (F-RCNN) deep learning algorithm is introduced to identify the position of the steel coil end-head for a hardware system set up for image sensing and detection. The feature pyramid network (FPN) and the parallel attention module (PAM), which are both involved in the traditional F-RCNN, are used to increase the detection accuracy. Our experimental results validated the effectiveness of the proposed improved algorithm.
Corresponding author: Chi-Hsin Yang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Jian-Zhou Pan, Chi-Hsin Yang, Long Wu, Wen-Hu Tang, and Kung-Chieh Wang, An Improved Faster Region-based Convolutional Neural Network Algorithm for Identification of Steel Coil End-head, Sens. Mater., Vol. 35, No. 10, 2023, p. 4653-4669. |
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