Detection probability is an extremely important performance metric for Automated Inspection (AI) systems. Using the detection of false connection points in patterned images as an example, this paper presents a novel method to estimate the detection capability of an AI system. One is concerned with how wide of a false connection can be reliably detected by a given AI system. One possible approach for evaluating detection probability is to compare automatic detection results with the results from manual human inspection. Unfortunately, this method is tedious, time consuming, and inspector-dependent. Moreover, an inspector's tiredness or oversight easily results in missing detection. In this paper, the Modulation Transfer Function (MTF) is used to determine the functional resolution of the system and generate theoretical profiles around false connection defects. The algorithm used for detecting the defects contains an auto- thresholding method for binarization. The statistical properties of these thresholds can be derived from the on-line record of thresholds of the system and essentially determine the detection results. Based on the statistical properties of the thresholds and their bounds, as well as the shapes of theoretical profiles, the detection probability of the AI system is evaluated.
KEYWORDS: Inspection, Process control, Image processing, Manufacturing, Defect detection, Control systems, Data processing, Sensors, Signal detection, Machine vision
Many industrial manufacturing processes are not well understood and are treated as `black art' with few experts able to control the process and ensure product quality. However, modern manufacturing companies are finding it increasingly difficult to compete in the global marketplace without better process understanding and control. Automated inspection systems for general manufacturing have become more feasible through technical advances, primarily in sensor and computing technology. However, these systems have been used almost exclusively for the detection and subsequent removal of well defined, discrete defects from the product; thus guaranteeing high quality for the customer. This paper describes a larger opportunity to affect operations by employing web inspection techniques to dynamically analyze manufacturing conditions rather than just detecting the presence of defective material. One can then keep the process under better control, thereby eliminating defects, ensuring product quality, and optimizing manufacturing time on the production line. Specific image and data processing techniques will be illustrated including product uniformity metrics, automatic determination of thresholds for blob analysis, and localization of repeating defects within production data. The benefit of these techniques will be demonstrated through `real-world' examples of web-based manufactured products.
The two most common approaches to image processing today are software-based systems which are flexible but very slow unless run on very expensive computer hardware and systems using special purpose hardware which can be very fast but are typically very inflexible and fairly expensive. This paper presents an intermediate approach: The use of inexpensive electronically-programmable logic devices (EPLDs) in appropriate architectures to do a wide range of image processing operations thereby providing both the speed of hardware-based systems and most of the flexibility of softwarebased systems. Since EPLDs can be reprogrammed quickly (from associated ROMs) a sequence of operations can be performed in near-real-time by loading a sequence of EPLD configuration files one after another. This paper illustrates these ideas by showing the internal programming needed for many common image processing operations as well as appropriate system architectures.
Conference Committee Involvement (13)
Image Processing: Machine Vision Applications VIII
10 February 2015 | San Francisco, California, United States
Image Processing: Machine Vision Applications VII
3 February 2014 | San Francisco, California, United States
Image Processing: Machine Vision Applications VI
5 February 2013 | Burlingame, California, United States
Image Processing: Machine Vision Applications V
25 January 2012 | Burlingame, California, United States
Image Processing: Machine Vision Applications IV
25 January 2011 | San Francisco Airport, California, United States
Image Processing: Machine Vision Applications III
20 January 2010 | San Jose, California, United States
Image Processing: Machine Vision Applications II
22 January 2009 | San Jose, California, United States
Image Processing: Machine Vision Applications
29 January 2008 | San Jose, California, United States
Machine Vision Applications in Industrial Inspection XV
29 January 2007 | San Jose, CA, United States
Machine Vision Applications in Industrial Inspection XIV
16 January 2006 | San Jose, California, United States
Machine Vision Applications in Industrial Inspection XIII
17 January 2005 | San Jose, California, United States
Machine Vision Applications in Industrial Inspection XII
21 January 2004 | San Jose, California, United States
Machine Vision Applications in Industrial Inspection XI
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