摘要
针对圆柱形蜂窝陶瓷侧面裂隙检测困难问题,提出一种基于机器视觉的检测方法。通过对侧面裂隙检测需求分析,选用COMS相机和LED白色平行光源。对采集的图像进行滤波处理,选择中值滤波去除椒盐噪声。根据图像的特点选择ROI区域,使用全局阈值分割算子threshold进行图像分割,采用膨胀方法连接断裂区域。在提取表面缺陷时,先用connection算子对图像区域分割,再选择面积、长度和宽度3个特征对表面缺陷进行提取。将本检测方法与人工检测方法比较分析,试验结果表明在检测样品均为50个时,本方法检测合格、不合格和混合样品所需时间分别为12.50、6.64和10.58 min,具有更高检测速度,实时性更好;准确率分别为96%、84%和90%,准确率还有待提升,需要进一步的研究。
Aiming at the difficulty of side crack detection of cylindrical honeycomb ceramics, a detection method based on machine vision is proposed. Through the demand analysis of side crack detection, COMS camera and LED white parallel light source are selected. The collected image is filtered, and the median filter is selected to remove salt and pepper noise. According to the characteristics of the image, the ROI region is selected, the global threshold segmentation operator threshold is used for image segmentation, and the expansion method is used to connect the fracture region. When extracting surface defects, the connection operator is used to segment the image region, and then three features of area, length and width are selected to extract surface defects. The test results show that when there are 50 samples, the time required for qualified, unqualified and mixed samples by this method is 12.50, 6.64 and 10.58 min respectively, which has higher detection speed and better real-time performance. The accuracy rates are 96%, 84% and 90% respectively. The accuracy rate needs to be improved and needs further research.
作者
毛卫平
高伟
顾寄南
雷文桐
胡君杰
方新领
Mao Weiping;Gao Wei;Gu Jinan;Lei Wentong;Hu Junjie;Fang Xinling(School of Mechanical Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《电子测量技术》
北大核心
2022年第2期117-122,共6页
Electronic Measurement Technology
基金
国家自然科学基金(51875266)项目资助。
关键词
机器视觉
圆柱形蜂窝陶瓷
图像处理
侧面裂隙
缺陷检测
machine vision
cylindrical honeycomb ceramics
image processing
side cracks
defect detection
作者简介
毛卫平,工学博士,副教授,硕士研究生导师,主要研究方向为机器视觉、运动控制。E-mail:13852986428@139.com;通信作者:高伟,硕士研究生,主要研究方向为机器视觉。E-mail:15195381929@163.com。
