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Automatic measurement of rebar spacing based on 3D point cloud segmentation using Rebar-YOLOv8-seg and depth data

•A new method for non-contact automatic measurement of rebar spacing.•Simultaneous measurement of multiple groups of rebar spacing is achieved based on image segmentation and RGB-D data fusion.•A rebar-specific segmentation model is proposed, achieving high accuracy and real-time performance in reba...

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Published in:Measurement : journal of the International Measurement Confederation 2025-01, Vol.242, p.116111, Article 116111
Main Authors: Song, Jiayin, Liao, Ting, Zhu, Qinglin, Wang, Jinlong, Yang, Liusong, Zhou, Hongwei, Lu, Teng, Jiang, Zhuoyuan, Song, Wenlong
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container_title Measurement : journal of the International Measurement Confederation
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creator Song, Jiayin
Liao, Ting
Zhu, Qinglin
Wang, Jinlong
Yang, Liusong
Zhou, Hongwei
Lu, Teng
Jiang, Zhuoyuan
Song, Wenlong
description •A new method for non-contact automatic measurement of rebar spacing.•Simultaneous measurement of multiple groups of rebar spacing is achieved based on image segmentation and RGB-D data fusion.•A rebar-specific segmentation model is proposed, achieving high accuracy and real-time performance in rebar segmentation.•The impact of data collection device height and angle on the measurement results was analyzed. In power transmission and transformation expansion projects, construction personnel must measure rebar spacing at construction sites to ensure the quality of concrete structures. Due to electrified equipment, high-precision steel rulers are prohibited. We introduced a new method for automatically measuring rebar spacing to improve construction safety and work efficiency. We developed the Rebar-YOLOv8-seg model to accurately extract the rebar image mask from complex backgrounds. Subsequently, the rebar mask was aligned with depth data to create a point cloud, which performed statistical filtering and principal component analysis. Finally, we used the Random Sample Consensus method to fit the point cloud centerline, then extracted key points and calculated rebar spacing. Experiments on 4 × 4 rebar measurements using the proposed method showed that, within a specific range of camera heights and shooting angles, the measured values of rebar spacing meet the engineering measurement requirements. The method achieves an average absolute error of 1.98 mm and an average relative error of 1.76 %. Additionally, multiple adjacent rebar spacings can be measured simultaneously and completed within 5 s, providing a new feasible approach for rebar spacing measurement.
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In power transmission and transformation expansion projects, construction personnel must measure rebar spacing at construction sites to ensure the quality of concrete structures. Due to electrified equipment, high-precision steel rulers are prohibited. We introduced a new method for automatically measuring rebar spacing to improve construction safety and work efficiency. We developed the Rebar-YOLOv8-seg model to accurately extract the rebar image mask from complex backgrounds. Subsequently, the rebar mask was aligned with depth data to create a point cloud, which performed statistical filtering and principal component analysis. Finally, we used the Random Sample Consensus method to fit the point cloud centerline, then extracted key points and calculated rebar spacing. 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subjects Automatic measurement
Depth data
Rebar spacing
YOLOv8-seg
title Automatic measurement of rebar spacing based on 3D point cloud segmentation using Rebar-YOLOv8-seg and depth data
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