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In-camera defect detection with applications to Web inspection systems.

机译：相机内缺陷检测以及Web检查系统的应用程序。

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摘要

One of the aims of industrial machine vision is to develop computer and electronic systems to replace human vision in quality control of industrial production. Traditionally these systems consist of a line scan camera, host computer, frame grabber and one or more dedicated processing boards. The work reported in this thesis develops defect detection algorithms for real-time processing of the camera video stream. The processing system is mounted inside the camera and provides sufficient defect detection capabilities to eliminate the need for an external frame grabber and other associated host computer peripheral systems. The system is targeted for web inspection but has the potential for broader application areas.; The output data from the camera is reduced by many orders of magnitude by only transmitting the “interesting” pixels of the image to be processed, and this can significantly reduce both the downstream processing hardware required and the bandwidth of the digital data received from the camera. The use of such special purpose cameras has the potential not only to improve the performance of machine vision systems for a wide variety of applications, but to improve the economic viability of these applications through reductions in hardware cost and complexity.; This real-time system must perform all of the required operations at the video bandwidth of the camera, and the work reported in this thesis uses hardware associated with the in-camera processing system, developed in the VLSI Laboratory at the University of Windsor, which includes programmable logic (Field Programmable Gate Array) directly connected to the video stream, and ancillary signal processing and control hardware (a DSP chip). These hardware limitations apply constraints to the algorithms, and we are almost always unable to use traditional image processing algorithms; rather we choose and develop algorithms based on their potential for identification based on minimal storage of a pixel-serial raster data.; In this thesis we report the following novel developments: (1) For non-textured background materials, three algorithms have been developed for the in-camera system: two (or multi) level thresholding; zero order background tracking; and delta modulation background tracking. (2) Auto-regressive techniques have been developed and implemented as a statistical approach to analyze textured backgrounds and to identify possible defects. This method of analysis has been extensively used to study visual textures. In the simplest form, the image is scanned to provide a one dimensional series of gray level fluctuations, which is treated as a one-dimensional stochastic process evolving in “time”. In a more comprehensive form, a pixel value is assumed to depend upon a certain part of its neighborhood. The coefficients of dependence are extracted using time series analysis techniques. (3) A novel algorithm for defect detect detection based on fuzzy fusion of texture features is developed, simulated and successfully implemented on the experimental test setup. Conventional approaches for web defect detection involve making “crisp” decisions for image analysis and recognition where imprecise or incomplete specifications are usually either ignored or discarded. The fuzzy logic algorithm uses imprecise or ambiguous image data caused by instrumental error or environmental noise such as dust or small variations in illumination to obtain a precise result. The developed algorithm can be applied to both textured and non textured materials and offers superior performance over traditional template matching methods.
机译：工业机器视觉的目标之一是开发计算机和电子系统，以取代人类视觉在工业生产的质量控制中。传统上，这些系统由行扫描摄像机，主机，抓帧器和一个或多个专用处理板组成。本文报道的工作开发了用于摄像机视频流实时处理的缺陷检测算法。该处理系统安装在相机内部，并提供足够的缺陷检测功能，从而无需使用外部图像采集卡和其他关联的主机外围设备。该系统的目标是进行网络检查，但具有广阔的应用领域。通过仅传输要处理图像的“有趣”像素，可以将来自摄像机的输出数据减少很多数量级，这可以显着减少所需的下游处理硬件和从摄像机接收的数字数据的带宽。这种特殊用途的相机的使用不仅有可能改善多种应用的机器视觉系统的性能，而且还可以通过降低硬件成本和降低复杂性来提高这些应用的经济可行性。该实时系统必须在摄像机的视频带宽上执行所有必需的操作，并且本文所报告的工作使用的是与温莎大学VLSI实验室开发的与摄像机内处理系统相关的硬件，包括直接连接到视频流的可编程逻辑（现场可编程门阵列），以及辅助信号处理和控制硬件（DSP芯片）。这些硬件限制对算法施加了限制，并且我们几乎总是无法使用传统的图像处理算法。相反，我们基于像素序列栅格数据的最小存储量来选择和开发基于识别潜力的算法。在这篇论文中，我们报告了以下新颖的进展：（1）对于非纹理背景材料，已经为相机内系统开发了三种算法：两级（或多级）阈值;零阶背景跟踪；和增量调制背景跟踪。 （2）已经开发出自动回归技术并将其作为统计方法来分析带纹理的背景并识别可能的缺陷。这种分析方法已被广泛用于研究视觉纹理。以最简单的形式，扫描图像以提供一维系列的灰度级波动，将其视为在“时间”内演化的一维随机过程。以更全面的形式，假定像素值取决于其邻域的特定部分。使用时间序列分析技术提取依赖系数。 （3）开发，模拟并基于实验测试设置成功实现了一种基于纹理特征模糊融合的缺陷检测新算法。用于检测Web缺陷的常规方法包括做出“清晰”的决定，以进行图像分析和识别，其中不精确或不完整的规范通常被忽略或丢弃。模糊逻辑算法使用由仪器误差或环境噪声（例如灰尘或照明的微小变化）引起的不精确或模糊的图像数据，以获得精确的结果。所开发的算法可应用于纹理材料和非纹理材料，并提供优于传统模板匹配方法的性能。

著录项

  • 作者

    Hajimowlana, Sayed Hossain.;

  • 作者单位

    University of Windsor (Canada).;

  • 授予单位 University of Windsor (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 201 p.
  • 总页数 201
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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