Semantic Segmentation with GLCM Images

Akira Nakajima, Hiroyuki Kobayashi

2024

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 predictions for a wide variety of raw concrete placement conditions at construction sites, compared to conventional segmentation methods.

Paper Citation

in Harvard Style

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, SciTePress, pages 527-531. DOI: 10.5220/0013072200003822

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Nakajima A.
AU - Kobayashi H.
PY - 2024
SP - 527
EP - 531
DO - 10.5220/0013072200003822
PB - SciTePress