周期纹理背景中的表面缺陷检测技术

基于机器视觉原理的自动光学表面缺陷检测技术是当今工业生产中在线检测表面缺陷的一种新的技术方法,是精密制造与组装工业过程中保证零部件表面质量的重要检测手段.以液晶面板TFT阵列表面缺陷自动光学检测为例,介绍了表面缺陷自动光学检测的基本组成原理,阐述了周期纹理背景表面上的表面缺陷检测方法、缺陷信息处理的基本过程与实用算法.针对表面缺陷检测图像处理技术难题,详细论述了表面缺陷扫描图像中的周期纹理背景傅里叶变换频域滤波方法、缺陷分割双阈值统计控制法,并用实验结果给出了例证.     

钢板表面缺陷检测光学系统的设计

为满足钢板表面缺陷在线检测系统宽幅面、高速、高分辨率的检测要求,优化设计了钢板表面缺陷视觉检测系统的光学部分.采用了一种新型LED线光源获得高强度均匀照明,多线阵CCD拼接成像完成幅面分割.明、暗域相结合的成像模式确保了大部分缺陷的有效检出.综合考虑光源、镜头与线阵CCD的影响,计算并优化选取了光学镜头的焦距、f数和视场角等参数以满足检测需要,整个光学系统设计满足在线检测需要并在样机中得以应用.     

PVC建材表面缺陷检测系统研究与设计

为满足PVC建材表面缺陷在线系统高速、高分辨率的检测要求,提出PVC建材表面缺陷检测的系统设计方案。为保证检测系统的实时性,硬件上采用面阵CCD摄像机与LED面光源相结合的图像采集装置,软件上采用实时处理和准实时处理相结合的图像处理方法。针对缺陷图像高噪声、直方图单峰明显的特征,提出边缘检测分割技术与数学形态处理相结合的算法流程。通过分析图像缺陷的统计特性,提取PVC建材表面缺陷区域的特征参数,利用基于决策树的分类器将缺陷分为气泡、色痕、裂痕等。实验结果验证算法的有效性和实时性,其缺陷的检出率达到95%以上,识别率达90%以上。     

Automatic defect detection of texture surface with an efficient texture removal network

Automated defect inspection of texture surface is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. We present a simple but powerful image transformation network to remove textures and highlight defects at full resolution. The simple full convolution network consists only of 3 × 3 regular convolution and several dilated convolution blocks, which makes it compact and able to capture multi-scale features effectively. To further improve the ability of the network to suppress texture and highlight defects, a polynomial loss function combining perceptual loss, structural similarity loss and image gradient loss is proposed. In addition, a semi-automatic annotation method mainly composed of wavelet transform and relative total variation is designed to generate a data set of image pairs containing the original texture image and the desired texture removal image. We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method. The method has been applied to the surface defect online detection system of an aluminum ingot milling production line, which effectively improves the surface inspection efficiency and product quality.     

基于机器视觉的液晶基板表面缺陷检测系统

介绍一种基于机器视觉的液晶玻璃基板质量在线检测系统。利用分布式视觉处理技术、采用模块化的图像处理系统设计,能够实现缺陷的精确提取与对缺陷的智能分类和分级,满足LCD液晶玻璃基板质量在线检测的需要。     

Automatic defect detection of metro tunnel surfaces using a vision-based inspection system

Due to the impact of the surrounding environment changes, train-induced vibration, and human interference, damage to metro tunnel surfaces frequently occurs. Therefore, accidents caused by the tunnel surface damage may happen at any time, since the lack of adequate and efficient maintenance. To our knowledge, effective maintenance heavily depends on the all-round and accurate defect inspection, which is a challenging task, due to the harsh environment (e.g., insufficient illumination, the limited time window for inspection, etc.). To address these problems, we design an automatic Metro Tunnel Surface Inspection System (MTSIS) for the efficient and accurate defect detection, which covers the design of hardware and software parts. For the hardware component, we devise a data collection system to capture tunnel surface images with high resolution at high speed. For the software part, we present a tunnel surface image pre-processing approach and a defect detection method to recognize defects with high accuracy. The image pre-processing approach includes image contrast enhancement and image stitching in a coarse-to-fine manner, which are employed to improve the quality of raw images and to avoid repeating detection for overlapped regions of the captured tunnel images respectively. To achieve automatic tunnel surface defect detection with high precision, we propose a multi-layer feature fusion network, based on the Faster Region-based Convolutional Neural Network (Faster RCNN). Our image pre-processing and the defect detection methods also promising performance in terms of recall and precision, which is demonstrated through a series of practical experimental results. Moreover, our MTSIS has been successfully applied on several metro lines.     

机器视觉表面缺陷检测综述

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。     

无标记印刷品质量在线检测方法研究

印刷品图像配准是实现印刷品自动检测的关键技术。以高分辨率印刷品图像为研究对象,将原始图像分割成多个兴趣区域,将兴趣区域的中心坐标与旋转角作为图像配准参数,通过变步长搜索算法获取被测图像与模板图像之间的最小旋转角;通过仿射变换将待检图像与模板图像进行配准并使用双向差影法计算两者的差影图像;通过二值化阈值与缺陷区域面积阈值进行缺陷识别。实验结果显示：该检测系统对合格品的检测准确率为96%,对不合格品的检测准确率为94%。     

Automated visual inspection of rolled metal surfaces

A prototype for an automated visual on-line metal strip inspection system is described. The system is capable of both detecting and classifying surface defects in copper alloy strips, and it has been installed for evaluation in a production line in a rolling mill. The image acquisition part of the system is based on a CCD line scan camera and condensing bright field illuminators. The inspection algorithms are based on morphological preprocessing and combined statistical and structural defect recognition. The image processing hardware consists of commercial modules. An analysis of this implementation is presented. A similar inspection principle has also been successfully applied to steel strip inspection.     

机器视觉在印刷缺陷在线检测中的应用与研究

为了提高印刷缺陷检测准确性与效率,满足缺陷检测的实时性,提出了机器视觉技术应用于印刷缺陷在线检测并研究检测过程中的相关问题.设计印刷缺陷在线检测的方案及以数字信号处理器DSP为核心的视觉在线检测系统的硬件结构组成.研究图像预处理、图像匹配与缺陷检测的算法并加以改进使其满足缺陷检测的实时性.开发印刷品缺陷视觉检测软件,对缺陷检测流程进行仿真,验证检测方案与图像处理算法的可行性.     

An IKB defect classification system for automated industrial radiographic inspection

Abstract: This paper presents work undertaken as part of a project concerned with the development of a fully automated industrial radiographic inspection system, based on both conventional image-processing techniques for the detection and analysis of defects in the radiographic image, and intelligent knowledge-based (1KB) techniques for the classification and evaluation of defect data against the quality assurance requirements of the inspection process. In this paper the 1KB defect classification system is presented. This system is based on a hierarchical frame-based knowledge representation and a backward-chaining production rule system. Examples of the frame structures, frame taxonomies and the data-driven procedures, which maintain the knowledge base are given, along with an outline of the defect classification rules and the inference mechanism for dealing with uncertainty by means of confidence factors.     

基于反向P-M扩散的钢轨表面缺陷视觉检测

研制了一种基于反向P-M（Perona-Malik）扩散的钢轨表面缺陷视觉检测装置,该装置可 自动获取钢轨表面图像,并实现实时检测与定位钢轨表面缺陷. 钢轨图像具有光 照变化、反射不均、特征少等特点,为了在运动过程中 从复杂的钢轨表面图像提取缺陷,首先将图像进行反向P-M扩散,然后将扩散后的图像与原图像进 行差分,从而减小了上述因素的影响,最后将差分图像进行二值化操作,根据 缺陷边缘特性和面积进行滤波,分割出缺陷图像. 实验仿真和现场测试结果表明,该方法能很好地识别块状缺陷和线状缺陷,并且检测速度、精度、识别 率和误检率都能很好地满足要求.     

Fabric defect detection using local contrast deviations

Defect inspection is a vital step for quality assurance in fabric production. The development of a fully automated fabric defect detection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to delicate features of defects complicated by variations in weave textures and changes in environmental factors (e.g., illumination, noise, etc.). Based on characteristics of fabric structure, an approach of using local contrast deviation (LCD) is proposed for fabric defect detection in this paper. LCD is a parameter used to describe features of the contrast difference in four directions between the analyzed image and a defect-free image of the same fabric, and is used with a bilevel threshold function for defect segmentation. The validation tests on the developed algorithms were performed with fabric images from TILDA’s Textile Texture Database and captured by a line-scan camera on an inspection machine. The experimental results show that the proposed method has robustness and simplicity as opposed to the approach of using modified local binary patterns (LBP).     

Defect detection in inhomogeneously textured sputtered surfaces using 3D Fourier image reconstruction

Texture analysis techniques have been used extensively for surface inspection, in which small defects that appear as local anomalies in textured surfaces must be detected. Traditional surface inspection methods are mainly concentrated on homogeneous textures. In this paper, we propose a 3D Fourier reconstruction scheme to tackle the problem of surface inspection on sputtered glass substrates that contain inhomogeneous textures. Such sputtered surfaces can be found in touch panels and liquid crystal displays (LCDs). Since an inhomogeneously textured surface does not have repetition, self-similarity properties in the image, a sequence of faultless images along with the inspection image are used to construct a 3D image so that the periodic patterns of the surface can be observed in the additional frame-axis. Bandreject filtering is used to eliminate frequency components associated with faultless textures in the spatial domain image, and the 3D inverse Fourier transform is then carried out to reconstruct the image. The resulting image can effectively remove background textures and distinctly preserve anomalies. This converts the difficult defect detection in complicated inhomogeneous textures into a simple thresholding in nontextured images. Experimental results from a number of sputtered glass surfaces have shown the efficacy of the proposed 3D Fourier image reconstruction scheme.     

橡胶密封件裂缝缺陷定位方法研究

为了实现在橡胶密封件表面缺陷的自动检测中快速有效地定位裂缝缺陷,根据密封件裂缝缺陷图像的特点,应用自适应阈值分割算法,结合改进的二值形态学并行细化算法,提出了一种橡胶密封件裂缝缺陷的定位方法。该方法可从复杂的密封件图像中分割出裂缝区域,并完成对裂缝缺陷的定位。实验表明,该方法获得的定位结果与实际裂缝位置相符,对于橡胶密封件表面缺陷检测与质量提高具有重要意义。     

基于分形特征的水果缺陷快速识别方法

计算机视觉和图象处理技术在水果自动分选和分级中起着重要的作用。因为缺陷检测的复杂性,水果表面缺陷的快速检测和识别一直是水果自动化分选和分级的障碍。在实数域分形盒维数计算方法的基础上,提出了双金字塔数据形式的盒维数快速计算方法。对于待识别水果图象的可疑缺陷区,提出用５个分形维数作为描述该区域粗糙度和纹理方向性的特征参数,并用所提出的快速计算方法进行计算,然后利用人工神经网络（ＢＰ）作为模式识别器,区     

轨道病害视觉检测：背景、方法与趋势

铁路作为国家重要基础设施、国民经济大动脉和大众化运输方式,对社会经济发展起着不可替代的支撑作用。轨道是铁路系统的重要组件,轨道病害检测是铁路工务部门的核心业务。传统的人工巡检不仅费时费力,而且检测结果容易受到各种主观因素的影响。因此,自动化轨道病害检测对维护铁路运输安全具有重要的现实意义。考虑到视觉检测在速度、成本和可视化等方面的优势,本文聚焦于轨道病害视觉检测技术。首先以广泛应用的无砟轨道为例介绍轨道的基本结构,对常见的轨道表观病害进行样例展示、成因分析和影响评价;简要梳理常见的自动化轨道检测技术的基本原理和应用场景,对轨道病害视觉检测面临的图像质量不均、可用特征较少和模型更新困难等主要挑战进行归纳;然后,依照前景模型、背景模型、盲源分离模型及深度学习模型的分类逻辑对轨道病害视觉检测领域的研究现状进行综述,简要介绍了各类方法的代表性工作,总结了各类方法的技术特点与应用局限性;最后,针对智能化铁路的发展需求,展望了未来轨道病害视觉检测技术的研究趋势,即利用小样本/零样本学习、多任务学习与多源异构数据融合等技术手段来解决当前视觉检测系统中的鲁棒性弱、虚警率高等问题。     

基于机器视觉的汽车精密零件缺陷检测

本文针对汽车精密零件外观质量检测的需求,利用机器视觉系统采集零件外观图像,建立数据平台。基于中值滤波和图像复杂度,提出了改进OTSU算法提取图像的外观缺陷特征,与样本库进行对比,以识别可能的外观缺陷,提高缺陷检测效率。并且,针对缺陷类型分析其影响因素,从而找到产生缺陷的原因,为零件加工过程的改善提供理论依据。同时,搭建了缺陷视觉检测平台,以汽车转向器内部套筒为对象,实现了精密零件外观缺陷在线缺陷检测。     

Automatic nevirapine concentration interpretation system using support vector regression

Follow-up of human immunodeficiency virus (HIV) patients treated with Nevirapine (NVP) is a necessary process to evaluate the drug resistance and the HIV mutation. It is also usually tested by immunochromatographic (IC) strip test. However, it is difficult to estimate the amount of drug the patient gets by visually inspection of color. In this paper, we propose an automatic interpretation system using a commercialized optical scanner. Several IC strips can be placed at any direction as long as they are on the scanner plate. There are three steps in the system, i.e., light intensity normalization, image segmentation and NVP concentration interpretation. We utilized the Support Vector Regression to interpret the NVP concentration. From the results, we found out the performance of the system is promising and better than that of the linear and nonlinear regression.     

IC真实缺陷的边界提取和缺陷尺寸与形状的表征

为了对ＩＣ制造中真实多余物缺陷进行分类与识别,ＩＣ多余物缺陷的特征提取是非常重要的一步,文中首先提出一种基于数学形态学的ＩＣ真实多余物缺陷边界的检测方法,其次对边界进行了链码描述,最后对边界所表示的多余物缺陷进行了尺寸测量与形状分析,在预处理阶段,利用彩色ＨＳＶ模型分割原ＩＣ图像,然后用图形学开运算消除背景噪音,对开后的结果图像进行形态膨胀及形态腐蚀运算,消除多余物缺陷中的小洞噪音以获得从复杂背景