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 36, Number 9(3) (2024)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4017-4028
S&M3781 Research Paper of Special Issue https://doi.org/10.18494/SAM5334 Published: September 30, 2024 Development of Segmentation Technology for Fall Risk Areas in Small-Scale Construction Sites Based on Bird’s-eye-view Images [PDF] Na Jong-ho, Lee Jae-kang, Shin Hyu-soung, and Yun Il-dong (Received August 23, 2024; Accepted September 25, 2024) Keywords: construction site, bird’s eye view, process area classification, risk area classification, instance segmentation
Construction sites have shown the highest incidence of safety accidents across industries in recent times. Small-scale sites, in particular, often operate without on-site safety managers, leading to significant safety oversights. This paper developed a method for identifying risk areas during construction procedures by using bird’s-eye-view image data throughout the construction cycle. Actual construction site images were collected and specific target objects were selected to create an AI training dataset. The segmentation model’s performance was validated, and a system was developed to identify fall risk areas by establishing interconnections between these target objects within the images. The findings of this study can help enhance compliance assessment with construction procedures and improve safety management oversight at small-scale construction sites.
Corresponding author: Lee Jae-kang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Na Jong-ho, Lee Jae-kang, Shin Hyu-soung, and Yun Il-dong, Development of Segmentation Technology for Fall Risk Areas in Small-Scale Construction Sites Based on Bird’s-eye-view Images, Sens. Mater., Vol. 36, No. 9, 2024, p. 4017-4028. |
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