基于机器视觉的缺陷在线检测系统的研究

本文研究了基于机器视觉玻璃表面缺陷检测方法与原理。在玻璃的生产过程中对玻璃进行图像采集,通过对图像的处理,对玻璃中存在的缺陷进行有效的处理,本系统在缺陷的提取上与缺陷的识别分类上分别运用的是一种改进的动态阈值算法与一种改进的BP神经网络算法。研究结果表明,改进的两种算法简单、速度快、系统抗干扰能力强,在检测精度,效率和准确度上都有较大的提高。     

基于机器视觉的匣钵缺陷检测系统

针对锂电材料行业匣钵缺陷检测需求,设计了一种基于机器视觉的匣钵缺陷检测系统。该系统结合了光度立体算法以及机器学习算法,使用光度立体算法检测匣钵的腐蚀和裂纹,使用机器学习算法检测匣钵的缺口,并通过大量的模型训练来进行缺陷判别。通过实验测试,该系统具有较好的匣钵缺陷检测效果,大大提高了检测效率。     

机器视觉在PCB缺陷检测中的应用

PCB是电子产品的载体,PCB表面的缺陷是影响整个产品质量的主要因素,直接关系到产品的性能和可靠性。为了实现PCB生产过程中缺陷检测的自动化,研究了用机器视觉检测技术检测PCB表面缺陷的方法。在检测中采用了4K灰度线阵CCD和高精度光学驱动平台构建的检测系统,获得了满意的效果。     

一种基于机器视觉的铝管缺陷检测系统

针对铝管生产过程中对准确、可量化的自动缺陷检测系统的迫切需要,本文引入一种由图像采集、缺陷检测、缺陷处理等模块组成的铝管缺陷检测系统。平板探测器获取由X光高压电源产生,穿过铝管的X射线并把所形成的数字图像通过USB端口发送至检测服务器。检测服务器使用机器视觉算法检测图像中的缺陷。当服务器检测到缺陷时,会向PCI板上指定位输出信号,报警装置接到信号后报警提醒工作人员。实验表明该系统能够自动、准确的标记出铝管中存在的缺陷,达到了系统的设计目标。     

基于机器视觉的零部件表面缺陷检测方法研究

针对零部件表面缺陷检测精度问题,提出一种基于机器视觉的零部件表面缺陷检测方法。传统的利用机器视觉对零部件表面缺陷检测方法中,由于零部件表面的光学反射特性,因此无法对零部件表面缺陷进行高精度的检测。提出的基于机器视觉的零部件表面缺陷检测方法引进了差影法检测模型,根据部件表面特征,利用分段线性灰度算法对部件表面细小的缺陷进行区域检测,并且结合了灰度共生矩阵的换算熵作为判定的依据,最终建立的缺陷检测模型是利用矩阵方位度和相似度之比进行高精度的检测。为了验证设计的基于机器视觉的零部件表面缺陷检测方法的有效性,通过仿真试验证明了该设计方法,结果表明该方法能够有效地解决零部件表面缺陷检测的精度问题。     

基于机器视觉的微细孔壁缺陷检测

为满足微细孔壁缺陷工业在线检测的快速性和准确性的要求,采用一种特殊的锥面镜成像部件获取孔壁图像,实现对微细孔壁缺陷的机器视觉检测。锥面镜成像过程将照明光路和成像光路合二为一,根据所获图像的特点设计出环形图像展开算法,转换为矩形图像后拼接出完整的孔壁图像。利用电控平移台和相机之间的触发配合,保证了图像采集的实时性和准确性。构建实验平台并对相关参数予以分析,实现了对直径为8mm、深为100mm微小孔壁缺陷的自动化检测,可以检测出直径0.3mm的缺陷,测量标准差为0.021,验证了后续工业化应用的可行性。     

基于机器视觉的表贴芯片缺陷检测系统研究

基于表贴芯片缺陷检测的需要,设计了一种基于机器视觉的表贴芯片缺陷检测系统,并对该系统的硬件和软件部分作了简要介绍,重点研究了芯片的引脚边缘检测算法,在此基础上实现了芯片长度、宽度和间距等特征参数的提取。测试结果表明,该检测系统能达到在线生产的工艺要求。     

基于机器视觉的钨棒曲面缺陷检测方法

本文重点设计了一套基于机器视觉的钨棒曲面缺陷检测与分类方法,有效解决了钨棒曲面缺陷肉眼检测分类效率低、误检率高、容易漏检等问题。首先设计了钨棒图像采集平台,用于获取钨棒曲面图像;然后通过Gamma校正、Otsu-Canny算法、形态学闭运算等图像处理技术提取缺陷区域图像;接下来利用灰度共生矩阵计算缺陷区域图像的纹理特征参数;最终用SVM支持向量机对钨棒曲面缺陷进行分类预测。实验预测结果表明,该系统对钨棒曲面缺陷分类准确率可达93.33%,能够基本满足工业现场需求,具有较高推广价值。     

基于机器视觉的凸轮轴表面缺陷检测系统

传统的凸轮轴表面缺陷多为产后抽检,相比在线全检存在漏检与缺陷率上升等现象,且只能事后发现;而人工在线全检不但会使成本上升、也对人力资源提出了考验。为此实现自动实时在线全检就成为急需解决的课题。设计了基于机器视觉的凸轮轴表面缺陷在线自动检测系统。系统安装在凸轮轴生产流水线两侧,搭建特定光源,在凸轮轴移动、停止、旋转过程中通过高速相机对其表面进行图像捕获,并由工控机进行缺陷判定与定位。根据轴类表面缺陷的特征,设计了缺陷分割算法和缺陷区域标记算法,对凸轮轴表面的外伤、砂眼、研磨不良等典型缺陷进行分辨。算法可以准确提取目标缺陷区域,标记缺陷位置并统计缺陷特征对缺陷进行判定。该系统可在0.44 s每根轴的速度下,检测出凸轮轴表面直径大于1 mm 的缺陷,并通过人机交互界面显示缺陷所在位置。完全可以取代产后抽检及人工在线全检,同时还可以提高检测效率与检测精度。     

An insect vision-based motion detection chip

The architectural and circuit design aspects of a mixed analog/digital very large scale integration (VLSI) motion detection chip based on models of the insect visual system are described. The chip comprises two one-dimensional 64-cell arrays as well as front-end analog circuitry for early visual processing and digital control circuits. Each analog processing cell comprises a photodetector, circuits for spatial averaging and multiplicative noise cancellation, differentiation, and thresholding. The operation and configuration of the analog cells is controlled by digital circuits, thus implementing a reconfigurable architecture which facilitates the evaluation of several newly designed analog circuits. The chip has been designed and fabricated in a 1.2-μm CMOS process and occupies an area of 2×2 mm²     

A vision-based fully automated approach to robust image cropping detection

The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content that is quite insensitive to image processing operations, such as compression and resizing, typical of social media platforms. Robust computer vision techniques are employed throughout, so as to cope with large sources of noise in the data and improve detection performance. The method leverages a novel metric based on robust statistics, and is also capable to decide autonomously whether the image at hand is tractable or not. The results of an extensive experimental evaluation covering several cropping scenarios demonstrate the effectiveness and robustness of our approach.     

Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis.

This paper presents a stereo vision system for the detection and distance computation of a preceding vehicle. It is divided in two major steps. Initially, a stereo vision-based algorithm is used to extract relevant three-dimensional (3-D) features in the scene, these features are investigated further in order to select the ones that belong to vertical objects only and not to the road or background. These 3-D vertical features are then used as a starting point for preceding vehicle detection; by using a symmetry operator, a match against a simplified model of a rear vehicle's shape is performed using a monocular vision-based approach that allows the identification of a preceding vehicle. In addition, using the 3-D information previously extracted, an accurate distance computation is performed.     

基于Gabor滤波器的喷丝板疵点检测

疵点检测是现代纺织工业产品质量控制中的关键环节之一,对保证聚酯纤维纺织品质量具有重要的现实意义.由此本文提出了一种基于Gabor滤波疵点检测方法.该检测方法通过Gabor滤波处理,使特定方向与特定频率的疵点位置初步被过滤呈现出来,之后通过边缘检测算法计算图像中待检测的疵点位置,准确获得疵点在图像中的位置.仿真实验结果表...     

Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera

Fall detection is one of the most important health care issues for elderly people at home, which can lead to severe injuries. With the advances and conveniences in computer vision in the last few decades, computer vision-based methods provide a promising way for detecting falls. In this paper, we propose a novel vision-based fall detection method for monitoring elderly people in house care environment. The foreground human silhouette is extracted via background modeling and tracked throughout the video sequence. The human body is represented with ellipse fitting, and the silhouette motion is modeled by an integrated normalized motion energy image computed over a short-term video sequence. Then, the shape deformation quantified from the fitted silhouettes is used as the features to distinguish different postures of the human. Finally, different postures are classified via a multi-class support vector machine and a context-free grammar-based method that provides longer range temporal constraints can verify the detected falls. Extensive experiments show that the proposed method has achieved a reliable result compared with other common methods.     

Implementation of vision-based planar grasp planning

This research describes the implementation of a vision-based algorithm that is capable of rapidly determining robotic grasp points for planar objects. A representation of the target and a quadtree expansion generate candidate grasps that are compared using a cost function. The approach returns the first acceptable grasp point at a given tree resolution. The system has an execution time on the order of seconds and it is suitable for a large number of planar or near planar objects     

基于阈值频率的激光超声缺陷检测

针对激光超声技术难以定量分析、检测金属样品缺陷的问题,根据不同缺陷深度处反射与透射表面波的频率交叉现象,提出了阈值频率,研究了表面缺陷深度与其对应波长的关系。通过小波分解和频谱分析,探讨和分析了反射波和透射波在频谱能量图中的波形分布特征。结果表明:由阈值频率计算的波长与缺陷深度之间的关系(即λ=4 h)与理论数据分析结果吻合良好;并随着缺陷深度的增加,波长也随之表现出线性增加的特性。由此可见,该分析方法达到了预期的效果,为表面缺陷深度的进一步定量表征提供了参考方向。     

Genetic algorithms for defect detection of flip chips

Flip chip packaging technology is widely used in high density assembly and superior performance devices. The solder joints are sandwiched between dies and substrates, leading to the defects optically opaque. Defect inspection of flip chips become more difficult. In this paper, a nondestructive detection method was presented. Ultrasonic excitations were forced on the surface of the flip chips and the raw vibration signals were measured by a laser scanning vibrometer. Eleven time domain features and twenty-four frequency domain features were extracted for analysis. After that, the genetic algorithm was introduced for feature selection and the back propagation network was adopted for classification and recognition. The flip chips were divided into three categories: good flip chips, flip chips with missing solder joints, and flip chips with open solder joints. They are recognized under the features selected by genetic algorithms rapidly and accurately, compared with those under other feature datasets, demonstrating that the approach using genetic algorithms is effective for defect inspection in flip chip packaging.     

面向引线框架缺陷的图像配准算法

为了降低引线框架缺陷识别的误检率,比较了基于灰度的模板匹配和基于特征的匹配算法之间的优缺点,并针对引线框架缺陷检测中参考图和检测图存在差异的特点,提出基于区域定位和不变矩的特征匹配算法。该算法通过边缘检测定位出特征区域,并用不变矩进行区域特征描述。在缺陷识别试验中,相比于模板匹配算法,该算法表现出更快的结算速度,更高的配准精度,更低的配准失败概率。结果表明,该配准算法适用于引线框架缺陷识别,降低误检率。     

基于HOG特征的织物瑕疵检测算法

传统织物瑕疵检测依赖人工,成本高且效率低.提出基于机器视觉和图像处理算法的织物瑕疵检测方法,采用方向梯度直方图描述织物纹理特征,通过计算局部区域的方向梯度直方图特征与无瑕疵织物图像特征的相似性来识别瑕疵,在织物图像数据集上的待测结果表明,此算法能够有效地提取出织物的瑕疵.