共查询到19条相似文献,搜索用时 156 毫秒
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以检测发动机喷嘴上几何尺寸为目的,设计了以TMS320DM642DSP为处理器,利用机器视觉技术进行检测的系统;通过CCD摄像机采集被测喷嘴的数字图像,在DSP中进行图像去噪、边缘检测、图像识别等图像处理过程,识别出被测尺寸并计算出值;阐述了该系统的工作原理和图像处理的算法,流程及实验结果。 相似文献
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运动目标尺寸实时检测与显示是运动图像检测和应用视觉研究领域的一个重要课题,而图像处理则是其核心内容;文章针对采集到的图像进行一系列的处理,最终实时显示图像并获得运动目标的尺寸等信息;首先针对采集图像时电子设备的不稳定性和检测模块的不一致性,进行中值滤波和增益校正同时将处理图像实时显示出来;然后对其整体图像进行分割与二值化、角点检测和边缘拟合处理,获得目标的尺寸信息;最后利用VC++将整体处理图像及尺寸信息显示出来,以便于图像理解和模式识别;实验证明,文章提出的算法、软件的设计很好地滤除不相关的目标,保存感兴趣目标,具有良好的实时性、精确性,能够满足当代工业检测设备中的要求。 相似文献
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针对油封的大批量、多品种、流水线作业的生产特点以及"零废品率"的检测要求,利用机器视觉检测和计算机图象处理技术,研究开发了适应大批量生产和在线实时检测的油封尺寸机器视觉检测系统;建立了系统软硬件结构,分析了系统的组成和工作原理及工作过程,并对图像处理算法进行了设计;系统标定后的实验检测结果证明,该检测系统能实现产品100%检测的生产目标,具有非接触、在线实时、精度适中、成本低等特点,具有较好的生产通用性。 相似文献
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基于虚拟仪器的产品标签视觉检测系统 总被引:2,自引:1,他引:1
针对生产过程中人工检测产品标签制约生产速度的情况,采用一种基于虚拟仪器的机器视觉产品标签检测系统,该系统以LabVIEW为平台,配以1394摄像头和图像采集卡,利用一定的图像处理技术,如平均滤波、平滑处理和局部增强处理等,完成视觉检测;经过测试,从图像采集到图像处理得出结论耗时不超过40ms,满足性能指标的要求,该系统开发周期短,检测速度快,充分发挥了虚拟仪器技术的功能强大、扩展性强的特点。 相似文献
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针对人工检测安瓿瓶包装质量时存在的速度慢以及受主观因素影响导致的准确率低等问题,提出一种机器视觉和轻量级卷积神经网络结合的安瓿瓶包装质量检测方法。首先,采用机器视觉中基于阈值分割以及仿射变换的方法对待测图片进行阈值处理、倾斜校正和安瓿瓶区域的裁剪;然后,根据图像特点以及缺陷识别要求设计分类算法的网络结构;最后,采集生产现场图片构建安瓿瓶包装缺陷数据集,之后对提出的安瓿瓶包装缺陷识别网络进行了验证,并测试了部署在Jetson Nano嵌入式平台上的算法的准确率及检测速度。实验结果表明:以每盒五支装的产品为例,所提安瓿瓶包装质量检测算法平均每盒耗时70.1 ms,即可达14盒/秒,而准确率为99.94%,能够实现在Jetson Nano嵌入式平台上的在线高精度安瓿瓶包装质量检测。 相似文献
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刘焕军 《计算机测量与控制》2011,19(2)
研究提出了新的玻璃制品智能检测系统和算法;根据玻璃制品检测的需要,设计了一个机器视觉检测系统,并开发了实验样机;在获取玻璃制品图像后,根据缺陷的特点来分割出可能缺陷区域,然后在可能缺陷区域内提取缺陷特征;提出采用一种多核函数支持向量机集成方法来对特征进行分类;此多核函数支持向量机集成采用遗传算法来协同优化集成中支持向量机的各项参数,使得各支持向量机在拥有较高分类性能的同时保持差异性;而在最后集成各支持向量机时采用了遗传选择集成方法;实验表明采用文中提出的检测算法在实验样机上检测玻璃制品质量,准确率可达97%以上. 相似文献
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针对人工检测安瓿瓶包装质量时存在的速度慢以及受主观因素影响导致的准确率低等问题,提出一种机器视觉和轻量级卷积神经网络结合的安瓿瓶包装质量检测方法。首先,采用机器视觉中基于阈值分割以及仿射变换的方法对待测图片进行阈值处理、倾斜校正和安瓿瓶区域的裁剪;然后,根据图像特点以及缺陷识别要求设计分类算法的网络结构;最后,采集生产现场图片构建安瓿瓶包装缺陷数据集,之后对提出的安瓿瓶包装缺陷识别网络进行了验证,并测试了部署在Jetson Nano嵌入式平台上的算法的准确率及检测速度。实验结果表明:以每盒五支装的产品为例,所提安瓿瓶包装质量检测算法平均每盒耗时70.1 ms,即可达14盒/秒,而准确率为99.94%,能够实现在Jetson Nano嵌入式平台上的在线高精度安瓿瓶包装质量检测。 相似文献
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Julio Molleda Rubén Usamentiaga Daniel F. García Francisco G. Bulnes Adrián Espina Bassiru Dieye Lyndon N. Smith 《Computers in Industry》2013
Measurement, inspection and quality control in industry have benefited from 3D techniques for imaging and visualization in recent years. The development of machine vision devices at decreased costs, as well as their miniaturization and integration in industrial processes, have accelerated the use of 3D imaging systems in industry. In this paper we describe how to improve the performance of a 3D imaging system for inline dimensional quality inspection of long, flat-rolled metal products manufactured in rolling mills we designed and developed in previous works. Two dimensional characteristics of rolled products are measured by the system: width and flatness. The system is based on active triangulation using a single-line pattern projected onto the surface of the product under inspection for range image acquisition. Taking the system calibration into account the range images are transformed into a calibrated point cloud representing the 3D surface reconstruction of the product. Two approaches to improve the line detection and extraction method used in the original system are discussed, one intended for high-speed processing with lower accuracy, and the other providing high accuracy while incurring higher computational time expenses. A mechanism to remove, or at least reduce, the effects of product movements while manufacturing, such as bouncing and flapping, is also proposed to improve the performance of the system. 相似文献
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基于计算机视觉的织物疵点自动检测 总被引:1,自引:0,他引:1
介绍了基于计算机视觉的织物疵点自动检测的工作原理;分析了织物疵点自动检测的功能结构和自动检测硬件组成框图;阐述了织物疵点图像的半阈值化处理、边缘检测和疵点特征求取方法;给出了织物疵点自动检测的程序框图、织物疵点的坯布图像及其疵点统计结果。 相似文献
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Design and application of industrial machine vision systems 总被引:2,自引:0,他引:2
In this paper, the role and importance of the machine vision systems in the industrial applications are described. First understanding of the vision in terms of a universal concept is explained. System design methodology is discussed and a generic machine vision model is reported. Such a machine includes systems and sub-systems, which of course depend on the type of applications and required tasks. In general, expected functions from a vision machine are the exploitation and imposition of the environmental constraint of a scene, the capturing of the images, analysis of those captured images, recognition of certain objects and features within each image, and the initiation of subsequent actions in order to accept or reject the corresponding objects. After a vision system performs all these stages, the task in hand is almost completed. Here, the sequence and proper functioning of each system and sub-systems in terms of high-quality images is explained. In operation, there is a scene with some constraint, first step for the machine is the image acquisition, pre-processing of image, segmentation, feature extraction, classification, inspection, and finally actuation, which is an interaction with the scene under study. At the end of this report, industrial image vision applications are explained in detail. Such applications include the area of automated visual inspection (AVI), process control, parts identification, and important role in the robotic guidance and control. Vision developments in manufacturing that can result in improvements in the reliability, in the product quality, and enabling technology for a new production process are presented. The key points in design and applications of a machine vision system are also presented. Such considerations can be generally classified into the six different categories such as the scene constraints, image acquisition, image pre-processing, image processing, machine vision justification, and finally the systematic considerations. Each aspect of such processes is described here and the proper condition for an optimal design is reported. 相似文献
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目的 工业产品的表面缺陷对产品的美观度、舒适度和使用性能等带来不良影响,所以生产企业对产品的表面缺陷进行检测以便及时发现并加以控制。机器视觉的检测方法可以很大程度上克服人工检测方法的抽检率低、准确性不高、实时性差、效率低、劳动强度大等弊端,在现代工业中得到越来越广泛的研究和应用。方法 以机器视觉表面缺陷检测为研究对象,在广泛调研相关文献和发展成果的基础上,对基于机器视觉在表面缺陷检测领域的应用进行了综述。分析了典型机器视觉表面缺陷检测系统的工作原理和基本结构,阐述了表面缺陷视觉检测的研究现状、现有视觉软件和硬件平台,综述了机器视觉检测所涉及到的图像预处理算法、图像分割算法、图像特征提取及其选择算法、图像识别等相关理论和算法研究,并对每种主要方法的基本思想、特点和存在的局限性进行了总结,对未来可能的发展方向进行展望。结果 机器视觉表面缺陷检测系统中,图像处理和分析算法是重要内容,算法各有优缺点和其适应范围。如何提高算法的准确性、实时性和鲁棒性,一直是研究者们努力的方向。结论 机器视觉是对人类视觉的模拟,机器视觉表面检测涉及众多学科和理论,如何使检测进一步向自动化和智能化方向发展,还需要更深入的研究。 相似文献