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Authors: Akira Nakajima 1 and Hiroyuki Kobayashi 2

Affiliations: 1 Graduate School of Robotics and Design, Osaka Institute of Technology, Osaka, Japan ; 2 Department of System Design, Osaka Institute of Technology, Osaka, Japan

Keyword(s): GLCM Images, Semantic Segmentation, U-Net, Ready-Mixed Concrete.

Abstract: At construction sites, there is a problem of excess ready-mixed concrete due to ordering errors being disposed of as industrial waste, and there is a need to introduce image recognition technology as an indicator to determine the appropriate amount to order. In this study, we attempted to detect ready-mixed concrete using a machine learning technique called semantic segmentation. We believe that texture analysis can solve the problem that raw concrete is difficult to recognize accurately because its texture is similar to that of other building materials and backgrounds and its texture fluctuates depending on the amount of moisture and mixing conditions. In this study, we proposed to perform texture analysis using GLCM (Gray Level Co-occurrence Matrix) and use the resulting image dataset. the results using GLCM images show that, compared to conventional segmentation, the GLCM images can be used to identify a variety of raw The results using the GLCM images provided highly accurate pre dictions for a wide variety of raw concrete placement conditions at construction sites, compared to conventional segmentation methods. (More)

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Paper citation in several formats:
Nakajima, A. and Kobayashi, H. (2024). Semantic Segmentation with GLCM Images. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7; ISSN 2184-2809, SciTePress, pages 527-531. DOI: 10.5220/0013072200003822

@conference{icinco24,
author={Akira Nakajima and Hiroyuki Kobayashi},
title={Semantic Segmentation with GLCM Images},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={527-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013072200003822},
isbn={978-989-758-717-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Semantic Segmentation with GLCM Images
SN - 978-989-758-717-7
IS - 2184-2809
AU - Nakajima, A.
AU - Kobayashi, H.
PY - 2024
SP - 527
EP - 531
DO - 10.5220/0013072200003822
PB - SciTePress