A Research on Machine Vision System Capability Evaluation based on Pixels

doi:10.3969/j.issn.1007-7375.e17-1088

Abstract

Abstract: In order to ensure the reliability of image data of irregular products similar to rectangular or oval, a general method is proposed to evaluate the capability of this kind of machine vision system (MVS) for shooting such products. The original image data is processed by the image processing technology to obtain the target area where the product is located. The number of pixels and the number of pixels at the boundary of the target region are considered as quality characteristics to determine whether the product is qualified or not. Then the capability of MVS is evaluated by analyzing the possible factors affecting its quality characteristics. A case is given to illustrate the efficiency and accuracy of this method.

Key words: machine vision system, image segmentation technology, the number of pixels, irregular products similar to rectangular or oval

CLC Number:

F406

SHI Liangxing, WANG Jie. A Research on Machine Vision System Capability Evaluation based on Pixels[J]. Industrial Engineering Journal , 2018, 21(2): 75-80,86.

References

[1] LI X Q, WANG L H, CAI N X. Machine-vision-based surface finish inspection for cutting tool replacement in production[J]. International Journal of Production Research, 2004, 42(11):2279-2287.
[2] 刘泽, 王嵬, 王平. 钢轨表面缺陷检测机器视觉系统的设计[J]. 电子测量与仪器学报, 2010, 24(11):1012-1017.LIU Ze, WANG Wei, WANG Ping. Design of machine vision system for inspection of rail surface defects[J]. Journal of Electronic Measurement and Instrument, 2010, 24(11):1012-1017.
[3] 秦国华, 易鑫, 李怡冉, 等. 刀具磨损的自动检测及检测系统[J]. 光学精密工程, 2014, 22(12):3332-3341.QIN Guohua, YI Xin, LI Yiran, et al. Automatic detection technology and system for tool wear[J]. Optics and Precision Engineering, 2014, 22(12):3332-3341.
[4] PERNG D B, LEE S M, CHOU C C. Automated bonding position inspection on multi-layered wire IC using machine vision[J]. International Journal of Production Research, 2010, 48(23):6977-7001.
[5] 孙雪晨, 姜肖楠, 傅瑶, 等. 基于机器视觉的凸轮轴表面缺陷检测系统[J]. 红外与激光工程, 2013, 42(6):1647-1653.SUN Xuechen, JIANG Xiaonan, FU Yao, et al. Surface defect detection system for camshaft based on computer vision[J]. Infrared and Laser Engineering, 2013, 42(6):1647-1653.
[6] IQBAL S M, GOPAL A, SANKARANARANAN P E, et al. Classification of selected citrus fruits based on color using machine vision system[J]. International Journal of Food Properties, 2016, 19(2):272-288.
[7] 杨建国, 肖蓉, 李蓓智, 等.基于机器视觉的刀具磨损检测技术[J]. 东华大学学报(自然科学版), 2012, 38(5):505-518.YANG Jianguo, XIAO Rong, LI Beizhi, et al. Tool wear based on machine vision[J]. Journal of DongHua University(Natural Science), 2012, 38(5):505-518.
[8] 赵宇峰, 高超, 王建国.基于机器视觉的工业产品表面缺陷检测算法研究[J]. 计算机应用与软件, 2012, 29(2):152-154.ZHAO Yufeng, GAO chao, WANG Jianguo. Research on machine vision based industrial product surface defect detection algorithm[J]. Computer Applications and Software, 2012, 29(2):152-154.
[9] TUNAK M, LINKA A. Directional defects in fabrics[J]. Research Journal of Textile and Apparel, 2008, 12(2):13-22.
[10] MAJESKE K D. Approval criteria for multivariate measurement systems[J]. Journal of Quality Technology, 2008, 40(2):140-153.
[11] 何桢, 生静, 施亮星. 测量系统的R&R分析在企业质量改进中的应用[J]. 工业工程, 2003, 6(1):62-66.HE Zhen, SHENG Jing, SHI Liangxing. Application of measurement system R&R analysis in quality improvement[J]. Industrial Engineering, 2003, 6(1):62-66.
[12] YUANGYAI C, KAEWSUWAN P, CHENG C Y. Color-based image analysis for statistical sausage production control[C]. Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference, 2012:1003-1011.
[13] SHI L X, REN H J, WANG J R, et al. Latin square design for chip length machine vision measurement system analysis[J]. Quality Engineering, 2016, 28(4):381-387.
[14] 李洪艳, 曹建荣, 谈文婷, 等.图像分割技术综述[J]. 山东建筑大学学报, 2010, 25(1):85-93.LI Hongyan, CAO Jianrong, TAN Wenting, et al. Review of image segmentation technology[J]. Journal of Shandong Jianzhu University, 2010, 25(1):85-93.
[15] 谢勰, 王辉, 张雪锋.图像阈值分割技术中的部分和算法综述[J]. 西安邮电学院学报, 2011, 16(03):1-5.XIE Xie, WANG Hui, ZHANG Xuefeng. A survey of partial sums algorithms in image thresholding techniques[J]. Journal of Xi'an University of Posts and Telecommunications, 2011, 16(03):1-5.
[16] 齐丽娜, 张博, 王战凯.最大类间方差法在图像处理中的应用[J]. 无线电工程, 2006, 36(7):25-27.QI Lina, ZHANG Bo, WANG Zhankai. Application of OTSU method in image processing[J]. Radio Engineering, 2006, 36(7):25-27.
[17] 王璇, 张帆, 程京.基于改进最大类间方差法的灰度图像分割[J]. 微计算机信息(嵌入式与SOC), 2010, 26(12-2):206-208.WANG Xuan, ZHANG Fan, CHENG Jing. Improved OTSU method on gray level image segmentation[J]. Microcomputer Information (Embedded and SOC), 2010, 26(12-2):206-208.