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作 者:闫伟伟 孙宏昌[1] 张廷 张树刚 田东阁 蒋永翔[1] YAN Weiwei;SUN Hongchang;ZHANG Ting;ZHANG Shugang;TIAN Dongge;JIANG Yongxiang(Institute of Robotics and Intelligent Equipment,Tianjin University of Technology and Education,Tianjin 300222,China;Hulun Buir Vocational Technical College,Hulun Buir 021000,China;Tianjin Yadong ZhixinTechnology Co.,Ltd.,Tianjin 300380,China)
机构地区:[1]天津职业技术师范大学机器人及智能装备研究所,天津300222 [2]呼伦贝尔职业技术学院,呼伦贝尔021000 [3]天津亚东智鑫科技有限公司,天津300380
出 处:《天津职业技术师范大学学报》2021年第1期37-41,共5页Journal of Tianjin University of Technology and Education
基 金:京津冀科技成果转化项目(17YFCZZC00270);天津市科学技术普及研发项目(17KPXMSF00190);天津职业技术师范大学科研发展基金资助项目(KJ2003).
摘 要:传统Canny算子在边缘检测和连接时,高低阈值由人工经验选定,缺乏自适应性,无法根据外部环境的变化,实时改变高低阈值。针对这一问题,本文提出一种利用基本全局阈值分割算法改进的Canny算子,并将其应用在编织布疵点检测系统中。通过检测系统自动适应外界条件以取得待检测图像的边界信息,最大程度地减少了因人为确定阈值而造成编织布边缘信息检测结果不准确的问题。实验结果表明:改进后的方法相对于传统方法,检测时间虽略有增加,但检测出的边缘像素明显增多,在边缘像素连接方面效果更好,且能在一定程度上抑制了假边缘,提高了Canny算子在织物疵点边缘检测上的准确性和自适应性。The conventional Canny operator in edge detection and connection,where the high and low thresholds are selected based on manual experience,lacks adaptability and cannot change the high and low thresholds in real time according to changes in the external environment.To address this problem,a Canny operator improved by the basic global threshold segmentation algorithm is proposed and applied to the woven fabric defect detection system.The detection system automatically adapts to external conditions to obtain the boundary information of the image to be detected,minimizing the problem of inaccurate detection results of woven fabric edge information caused by artificially determining the threshold.The experiment results show that by using the improved method,the detected edge pixels are significantly increased despite a slight increase in detection time compared to those of traditional methods.Besides,the effect of edge pixel connection is better,false edges are suppressed to a certain extent and the accuracy and adaptability of Canny operator in edge detection of fabric defects are improved.
关 键 词:全局阈值分割 边缘检测 改进Canny算子 python-OpenCV
分 类 号:TH69[机械工程—机械制造及自动化]
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