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Article

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

1
State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
COMAC ShangHai Aircraft Design and Research Institute, Shanghai 201210, China
3
China National Aeronautical Ratio Electronics Research Institute, Shanghai 200241, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 644; https://doi.org/10.3390/s19030644
Received: 4 January 2019 / Revised: 28 January 2019 / Accepted: 31 January 2019 / Published: 4 February 2019
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: flaw detection; automatic visual inspection; machine vision; feature extraction; support vector machine (SVM) flaw detection; automatic visual inspection; machine vision; feature extraction; support vector machine (SVM)
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MDPI and ACS Style

Zhou, Q.; Chen, R.; Huang, B.; Liu, C.; Yu, J.; Yu, X. An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods. Sensors 2019, 19, 644. https://doi.org/10.3390/s19030644

AMA Style

Zhou Q, Chen R, Huang B, Liu C, Yu J, Yu X. An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods. Sensors. 2019; 19(3):644. https://doi.org/10.3390/s19030644

Chicago/Turabian Style

Zhou, Qinbang, Renwen Chen, Bin Huang, Chuan Liu, Jie Yu, and Xiaoqing Yu. 2019. "An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods" Sensors 19, no. 3: 644. https://doi.org/10.3390/s19030644

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