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.
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