共查询到19条相似文献,搜索用时 390 毫秒
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针对一类薄壁金属管端部倒角后表面缺陷和尺寸检测要求,提出了一套基于视觉的检测方案,利用被测物体表面对光的反射特性,巧妙地获取倒角面清晰图像,运用MIL图像处理库中的不同函数,在VC6.0平台下开发了一套能够检测倒角面各种缺陷(凹坑、划痕、毛刺、切偏等)的视觉检测系统. 相似文献
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基于模式识别的零件表面瑕疵图像提取的设计与实现 总被引:2,自引:1,他引:1
针对精密零件表面瑕疵处理问题,将计算机视觉技术用于精密零件表面瑕疵图像的提取和分析,提高了生产中机械零件自动识别的实时性和分拣的准确率,并结合嵌入式系统进行控制。设计了一个基于机器视觉的零件表面瑕疵图像自动识别系统,采用图像处理及模式匹配的方法,实现了零件表面瑕疵图像的提取,为零件表面瑕疵的处理做好了准备工作,达到了对许多加工件和产品表面质量进行快速检测的目的。 相似文献
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利用PXI总线控制器以及IEEE-1394接口的图像采集设备构建机器视觉系统,应用计算机视觉以及数字图像处理技术实现了墙地砖表面缺陷的自动检测及等级分类。在墙地砖自动检测应用程序的设计中,首先应用IMAQ Vision Assistant进行主要算法的开发,再在LabVIEW的开发环境下对算法程序进行更加柔性的配置,并开发出具有人性化的界面,便于控制和处理。所研究的系统能对墙地砖表面疵点、缺损、鼓泡和裂痕等缺陷进行实时检测及等级分类,并得出详细的缺陷检测报告以及实现数据库管理等功能。 相似文献
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针对管屏多道焊缝表面缺陷存在焊缝定位困难、计算量过大的问题,文中提出一种管屏拼焊焊缝表面缺陷激光视觉检测方法,建立基于激光视觉的管屏焊缝检测系统,并基于激光视觉相机拍摄管屏图像。分析管屏特征并结合形态学操作定位焊缝,使用高维多项式拟合解决焊缝缺陷快速检测问题,实现了对管屏拼焊表面缺陷的快速检测。结果表明,该检测方案能识别的最小缺陷为1 mm2,检测速度为0.438 m/s,提升了管屏焊缝表面缺陷的检测效率,解决了曲面焊缝定位难、数据量大的难题,实现快速对大量焊缝轮廓进行检测,并符合应用要求。
创新点: (1)提出了一种管屏拼焊焊缝表面缺陷激光视觉检测方案,提升管屏焊缝表面缺陷检测效率。
(2)提出了基于高阶曲面的焊缝缺陷检测方法,实现快速对大量焊缝轮廓进行检测。
(3)管屏拼焊焊缝表面缺陷激光视觉检测方法经过数据集验证,效率和精度均满足应用要求。 相似文献
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数字图像处理技术与模式识别技术结合,可广泛应用于工业产品的分类识别。针对轴承在线装配生产中存在的缺陷检测识别的需求,研究基于机器视觉的自动检测系统,采用面阵相机对不同表面进行对比测量,对采集的图像进行二值化处理,为后续的图像预处理、模式识别、特征提取及特征值对比做了分析和准备,以实现产品的在线表面缺陷检测识别。 相似文献
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针对MAG焊特点,利用被动式熔池视觉传感系统,采集到清晰的熔池图像;通过同步对比试验方法,系统地对焊接气孔缺陷与熔池图像特征信息之间的关系进行了研究,提取了表面气孔、内部气孔(夹渣)对应的熔池图像特征,并从灰度均值和标准差的角度研究了焊接气孔缺陷产生过程中熔池图像变化情况及奇异特征.试验表明,通过熔池视觉图像特征判断气孔焊接缺陷,具有良好的可行性,为基于视觉传感的焊接缺陷自动识别提供了技术依据;一种图像特征可能预示有多种焊接缺陷产生的可能,一种焊接缺陷可能有多种图像特征显示. 相似文献
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建立一种基于机器视觉的精密轴承表面缺陷光学检测系统。利用图像展开和拼接技术获得轴承侧面完整而又没有重复的二维图像,在此基础上对微小轴承表面缺陷进行检测、缺陷提取和分类。实验结果表明:采用该方法能够快速、高效地检测出微小精密轴承表面大于10μm的缺陷形貌;能够准确地对凹坑、裂纹和划痕缺陷进行分类。 相似文献
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利用机器视觉技术检测线缆表面缺陷时,检测时间长、漏检率高。为此,提出一种基于机器视觉的线缆表面缺陷快速检测算法。通过引入CV-Kmeans区域分类算法建立自适应滤波窗口改进高斯滤波算法,在此基础上建立自适应模板,然后计算原图像与模板的Pearson(皮尔逊)相关系数快速判断图像是否含有缺陷。对含有缺陷的图像进行模板与原图差分,最后对差分所得到的图像用自适应阈值分割法提取缺陷。实验表明,算法可有效识别缺陷并减少检测时间,漏检率为3.22%,满足线缆生产需求。 相似文献
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A vision system for surface roughness characterization using the gray level co-occurrence matrix 总被引:4,自引:0,他引:4
Computer vision technology has maintained tremendous vitality in many fields. Several investigations have been performed to inspect surface roughness based on computer vision technology. This work presents a new approach for surface roughness characterization using computer vision and image processing techniques. A vision system has been introduced to capture images for surfaces to be characterized and a software has been developed to analyze the captured images based on the gray level co-occurrence matrix (GLCM).Three standard specimens and 10 machined samples with different roughness values have been characterized by the presented approach. Three-dimensional plots of the GLCMs for various captured images have been introduced, compared and discussed. In addition, some statistical parameters (maximum occurrence of the matrix, maximum occurrence position and standard deviation of the matrix) have been calculated from the GLCMs and compared with the arithmetic average roughness Ra. Furthermore, a new parameter called maximum width of the matrix is introduced to be used as an indicator for surface roughness. 相似文献
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In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy. 相似文献
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H. I. Shafeek E. S. Gadelmawla A. A. Abdel-Shafy I. M. Elewa 《NDT & E International》2004,37(4):291-299
In this paper, a novel automated vision system is introduced to detect and assess the welding defects of gas pipelines from the radiographic films. The proposed vision system was used to capture images for the radiographic films and apply various image processing and computer vision algorithms to detect the welding defects and to calculate necessary information such as length, width, area and perimeter of the defects. A developed software, named AutoWDA, has been fully written in lab using Microsoft Visual C++6 to perform the analysis process. The proposed system offers many advantages such as enhancing the captured images so that the defects appear much clear and eliminating the loss of image details, which occurs due to film deterioration by the time, by transferring the radiographic films into digitized images, which could be saved on magnetic mass storage media. The proposed system is considered quite cheap compared with commercial radiographic image enhancement systems. 相似文献