An automatic rebar spacing measuring method based on the YOLOv8-GB model

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2024-11-21 DOI:10.1016/j.measurement.2024.116278

Jiayin Song , Teng Lu , Ting Liao , Zhuoyuan Jiang , Qinglin Zhu , Jinlong Wang , Liusong Yang , Hongwei Zhou , Wenlong Song

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引用次数: 0

Abstract

In engineering construction projects, rebar spacing measurement requires significant manual labor with low efficiency. This paper proposes a new intelligent rebar spacing measurement method based on the YOLOv8-GB model to save the workforce and improve efficiency. This method collects images of rebars to be measured using a binocular camera, utilizes the proposed YOLOv8-GB model to extract rebars from the scene, and achieves spacing measurement. The system is deployed on the NVIDIA Jetson TX2 NX for on-site portable measurement and can run in real-time at 24 frames per second. Experimental results show that the improved YOLOv8-GB network, compared with the YOLOv8n network, increased Recall, Precision, [email protected], and mAP50-95 by 0.6 %, 5.5 %, 2.3 %, and 7.6 %, respectively. The measurement system built with YOLOv8-GB achieved an average absolute error of ± 1.7 mm, ±2.1 mm, and ± 2.7 mm for rebar spacing measurements on three different ground textures, with average relative errors of 0.85 %, 0.93 %, and 1.32 %, meeting engineering requirements. Compared to the measurement system built with YOLOv8n, the average absolute error decreased by 37.0 %, 8.0 %, and 25.0 % under the three different ground textures, while the average relative error decreased by 36.1 %, 8.8 %, and 23.7 %, respectively.

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基于 YOLOv8-GB 模型的钢筋间距自动测量方法

在工程建设项目中，钢筋间距测量需要大量的人工劳动，效率较低。本文提出了一种基于 YOLOv8-GB 模型的新型智能钢筋间距测量方法，以节省劳动力并提高效率。该方法使用双目摄像头采集待测钢筋图像，利用提出的 YOLOv8-GB 模型从场景中提取钢筋，并实现间距测量。该系统部署在 NVIDIA Jetson TX2 NX 上，用于现场便携式测量，可以每秒 24 帧的速度实时运行。实验结果表明，与 YOLOv8n 网络相比，改进后的 YOLOv8-GB 网络在 Recall、Precision、[email protected] 和 mAP50-95 方面分别提高了 0.6%、5.5%、2.3% 和 7.6%。使用 YOLOv8-GB 构建的测量系统在三种不同地面纹理上测量钢筋间距时，平均绝对误差分别为 ± 1.7 mm、±2.1 mm 和 ± 2.7 mm，平均相对误差分别为 0.85 %、0.93 % 和 1.32 %，满足工程要求。与使用 YOLOv8n 建立的测量系统相比，在三种不同的地面纹理下，平均绝对误差分别减少了 37.0 %、8.0 % 和 25.0 %，平均相对误差分别减少了 36.1 %、8.8 % 和 23.7 %。

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.