Next Article in Journal
An Occlusion-Aware Framework for Real-Time 3D Pose Tracking
Next Article in Special Issue
Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
Previous Article in Journal
Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China
Previous Article in Special Issue
Automated Calibration Method for Eye-Tracked Autostereoscopic Display
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(8), 2732; https://doi.org/10.3390/s18082732

A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

1
School of Electronics and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an 710049, China
2
Guangdong Xi’an Jiaotong University Academy, No. 3, Daliangdesheng East Road, Foshan 528000, China
*
Author to whom correspondence should be addressed.
Received: 16 July 2018 / Revised: 16 August 2018 / Accepted: 18 August 2018 / Published: 20 August 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
Full-Text   |   PDF [17920 KB, uploaded 20 August 2018]   |  

Abstract

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site. View Full-Text
Keywords: machine vision; steel bars; quality inspection; dimensional measurement; number counting; high precision; video data acquisition machine vision; steel bars; quality inspection; dimensional measurement; number counting; high precision; video data acquisition
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Zhang, J.; Ma, M.; Chen, Z.; Yue, S.; He, T.; Xu, X. A High Precision Quality Inspection System for Steel Bars Based on Machine Vision. Sensors 2018, 18, 2732.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top