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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 36, Number 5(3) (2024)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 2033-2043
S&M3651 Research Paper of Special Issue https://doi.org/10.18494/SAM4717 Published: May 31, 2024 Identification Methods for Structural Problems of Bridges Based on Deep Convolutional Neural Network [PDF] Gang Liu, Shuri Cai, Han Wei, Hongxiang Guo, Cairong Ni, and Zhensong Ni (Received October 20, 2023; Accepted May 17, 2024) Keywords: soft-nonmaximum suppression (Soft-NMS), faster regional convolutional neural network (Faster R-CNN), structural problems of bridges, identification network model
The safety of bridges, which are key components of roads, has attracted the attention of experts, technical engineers, and maintenance managers. Various apparent problems such as cracks, voids and pits, white precipitate, and corrosion must be identified during the visual inspection of a bridge. In this study, using soft-nonmaximum suppression (NMS), we improved the original NMS algorithm of the faster regional convolutional neural network (Faster R-CNN) and built a fine identification network model to classify and identify the structural problems of bridges to effectively reduce the missed detection rate. In addition, through manual inspection by photography, the average accuracies of the identification of problems, namely, voids and pits, cracks, and white precipitate, can reach 80.86, 81.42, and 87.39%, respectively, which are about 20 percentage points higher than that of the original Faster R-CNN.
Corresponding author: Shuri Cai  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Gang Liu, Shuri Cai, Han Wei, Hongxiang Guo, Cairong Ni, and Zhensong Ni, Identification Methods for Structural Problems of Bridges Based on Deep Convolutional Neural Network, Sens. Mater., Vol. 36, No. 5, 2024, p. 2033-2043. |
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