Cross-Section Dimension Measurement of Construction Steel Pipe Based on Machine Vision

Fuxing Yu (), Zhihu Qin, Ruina Li and Zhanlin Ji
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Fuxing Yu: College of Artifificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Zhihu Qin: College of Artifificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Ruina Li: College of Artifificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Zhanlin Ji: College of Artifificial Intelligence, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2022, vol. 10, issue 19, 1-14

Abstract: Currently, the on-site measuring of the size of a steel pipe cross-section for scaffold construction relies on manual measurement tools, which is a time-consuming process with poor accuracy. Therefore, this paper proposes a new method for steel pipe size measurements that is based on edge extraction and image processing. Our primary aim is to solve the problems of poor accuracy and waste of labor in practical applications of construction steel pipe inspection. Therefore, the developed method utilizes a convolutional neural network and image processing technology to find an optimum solution. Our experiment revealed that the edge image that is proposed in the existing convolutional neural network technology is relatively rough and is unable to calculate the steel pipe’s cross-sectional size. Thus, the suggested network model optimizes the current technology and combines it with image processing technology. The results demonstrate that compared with the richer convolutional features (RCF) network, the optimal dataset scale (ODS) is improved by 3%, and the optimal image scale (OIS) is improved by 2.2%. At the same time, the error value of the Hough transform can be effectively reduced after improving the Hough algorithm.

Keywords: steel tube measuring; dimension survey; edge detection; convolutional neural network; connected domain; circle detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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