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

Sensors

Jie Yu¹  Xiaoqing Yu²  Renwen Chen³  Chuan Liu³  Qinbang Zhou³  Bin Huang³ 

[1] COMAC ShangHai Aircraft Design and Research Institute, Shanghai 201210, China;China National Aeronautical Ratio Electronics Research Institute, Shanghai 200241, China;State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

关键词: flaw detection;    automatic visual inspection;    machine vision;    feature extraction;    support vector machine (SVM);   

DOI  :  10.3390/s19030644

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown