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An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by hu...

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Autores principales: Zhou, Qinbang, Chen, Renwen, Huang, Bin, Liu, Chuan, Yu, Jie, Yu, Xiaoqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386913/
https://www.ncbi.nlm.nih.gov/pubmed/30720719
http://dx.doi.org/10.3390/s19030644
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author Zhou, Qinbang
Chen, Renwen
Huang, Bin
Liu, Chuan
Yu, Jie
Yu, Xiaoqing
author_facet Zhou, Qinbang
Chen, Renwen
Huang, Bin
Liu, Chuan
Yu, Jie
Yu, Xiaoqing
author_sort Zhou, Qinbang
collection PubMed
description Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
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spelling pubmed-63869132019-02-26 An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods Zhou, Qinbang Chen, Renwen Huang, Bin Liu, Chuan Yu, Jie Yu, Xiaoqing Sensors (Basel) Article Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles. MDPI 2019-02-04 /pmc/articles/PMC6386913/ /pubmed/30720719 http://dx.doi.org/10.3390/s19030644 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Qinbang
Chen, Renwen
Huang, Bin
Liu, Chuan
Yu, Jie
Yu, Xiaoqing
An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
title An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
title_full An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
title_fullStr An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
title_full_unstemmed An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
title_short An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods
title_sort automatic surface defect inspection system for automobiles using machine vision methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386913/
https://www.ncbi.nlm.nih.gov/pubmed/30720719
http://dx.doi.org/10.3390/s19030644
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