聚晶金刚石复合片表面裂纹视觉检测技术研究
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摘要
采用计算机视觉检测技术提取出表面缺陷特征量,完成聚晶金刚石复合片表面裂纹缺陷检测。首先,根据聚晶金刚石复合片表面特性,研究合适的光源照明系统。然后,提出一种基于直方图投影梯度极值的局部边界提取方法,将感兴趣区域进行提取。在此基础上,采用图像滤波、阈值分割的方法实现裂纹的准确提取。最后,通过计算裂纹连通域的圆形度和长宽比进行裂纹识别。实验结果表明,本方法可有效地对聚晶金刚石复合片表面裂纹缺陷进行检测。
Computer vision detection technology was adopted to extract the defect feature quantity of the surface,and further completed the detection of surface crack defects in polycrystalline diamond compact.First,according to the features of polycrystalline diamond compact,the appropriate light source lighting system was studied.Then,a local boundary extraction method based on the histogram projection gradient extremum was proposed,and the region of interest(ROI)was extracted.On this basis,the method of image filtering and threshold segmentation were used to realize the accurate extraction of the crack.Finally,the crack was identified by calculating the circularity factor and aspect ratio of the cracked domain.Experimental results showed that this method can effectively detect the surface crack defects of polycrystalline diamond compact.
Computer vision detection technology was adopted to extract the defect feature quantity of the surface,and further completed the detection of surface crack defects in polycrystalline diamond compact.First,according to the features of polycrystalline diamond compact,the appropriate light source lighting system was studied.Then,a local boundary extraction method based on the histogram projection gradient extremum was proposed,and the region of interest(ROI)was extracted.On this basis,the method of image filtering and threshold segmentation were used to realize the accurate extraction of the crack.Finally,the crack was identified by calculating the circularity factor and aspect ratio of the cracked domain.Experimental results showed that this method can effectively detect the surface crack defects of polycrystalline diamond compact.
引文
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15 Hu Huanxing.Research of technology on surface defects detection for magnetic tile based on machine vision[D].Nanchang:Nanchang University,2015(in Chinese).胡环星.基于机器视觉的磁瓦表面缺陷检测技术研究[D].南昌:南昌大学,2015.
16 Kong Xiangwei,Qu Xinghua,Zhang Liping,et al.Image processing of the precision measurement on metal surface production defect[J].Nanotechnol Precision Eng,2008,6(4):267(in Chinese).孔祥伟,曲兴华,张立平,等.精确测量金属镀层工件表面缺陷的图像处理方法[J].纳米技术与精密工程,2008,6(4):267.
17 Otsu N.A threshold selection method from gray-level histograms[J].Systems Man Cybernetics IEEE Transactions on,1979,9(1):62.
2 Yahiaoui M,Gerbaud L,Paris J Y,et al.A study on PDC drill bits quality[J].Wear,2013,298-299(1):32.
3 Jeon Y J,Yun J P,Choi D C,et al.Defect detection algorithm for corner cracks in steel billet using discrete wavelet transform[C]∥ICROS-SICE International Joint Conference 2009.Fukuoka Int Congress Center,2009:2769.
4 Li Y,Dhakal S,Peng Y.A machine vision system for identification of micro-crack in egg shell[J].J Food Eng,2012,109(1):127.
5 Adhikari R S,Moselhi O,Bagchi A.Image-based retrieval of concrete crack properties for bridge inspection[J].Automation Constr,2014,39(4):180.
6 Li Rongxuan,Shen Xizhong,Zhang Shuhang,et al.Surface crack detection of shaft components based on image processing[J].J Graphics,2015(1):62(in Chinese).厉荣宣,沈希忠,张树行,等.基于图像处理的轴类零件表面裂纹检测[J].图学学报,2015(1):62.
7 Fu Bangrui.Image detection on surface crack of billet[D].Chengdu:University of Electronic Science and Technology of China,2012(in Chinese).付邦瑞.钢坯表面裂纹图像检测[D].成都:电子科技大学,2012.
8 Zhang Junxiong,Xun Yi,Li Wei.Detection of surface cracks of corn kernel based on morphology[J].Optics Precision Eng,2007,15(6):951(in Chinese).张俊雄,荀一,李伟.基于形态特征的玉米种子表面裂纹检测方法[J].光学精密工程,2007,15(6):951.
9 Wang Yiwen.Research of steel ball surface detection key technology and development of prototype[D].Harbin:Harbin University of Science and Technology,2010(in Chinese).王义文.钢球表面缺陷检测关键技术研究及样机研制[D].哈尔滨:哈尔滨理工大学,2010.
10 Li Wubin.Research on vision-based online detection algorithm for surface defects of hot rolled steel bar[D].Jinan:Shandong University,2013(in Chinese).李武斌.热轧圆钢表面缺陷视觉在线检测算法研究[D].济南:山东大学,2013.
11 Zhang Hongtao.Research of key technology on on-line surface defects detection system for steel plate based on computer vision[D].Tianjin:Tianjin University,2008(in Chinese).张洪涛.钢板表面缺陷在线视觉检测系统关键技术研究[D].天津:天津大学,2008.
12 Gao Min,Wu Fupei,Li Shengping.Region of interest extraction algorithm based on gray-level projection histogram[J].J Shantou University(Nat Sci),2012,27(4):54(in Chinese).高敏,吴福培,李昇平.基于投影直方图提取目标感兴趣区域的新方法[J].汕头大学学报(自然科学版),2012,27(4):54.
13 Gonzalez R C,Woods R E.数字图像处理(MATLAB版)[M].阮秋琦,等,译.北京:电子工业出版社,2005:72.
14 Qian Weixin,Liu Ruigen,Wang Wanli.An improved method of adaptive median filter[J].Optics Optoelectron Technol,2011,9(4):35 (in Chinese).钱伟新,刘瑞根,王婉丽.一种改进的自适应中值滤波算法[J].光学与光电技术,2011,9(4):35.
15 Hu Huanxing.Research of technology on surface defects detection for magnetic tile based on machine vision[D].Nanchang:Nanchang University,2015(in Chinese).胡环星.基于机器视觉的磁瓦表面缺陷检测技术研究[D].南昌:南昌大学,2015.
16 Kong Xiangwei,Qu Xinghua,Zhang Liping,et al.Image processing of the precision measurement on metal surface production defect[J].Nanotechnol Precision Eng,2008,6(4):267(in Chinese).孔祥伟,曲兴华,张立平,等.精确测量金属镀层工件表面缺陷的图像处理方法[J].纳米技术与精密工程,2008,6(4):267.
17 Otsu N.A threshold selection method from gray-level histograms[J].Systems Man Cybernetics IEEE Transactions on,1979,9(1):62.