A knowledge-based machine vision system for automated industrial web inspection

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Due to the requirement for real-time operation, these systems are typically implemented in special purpose hardware that performs very specific operations. Hence, these systems inflexible in the sense that they cannot easily be adapted to other applications. However, current trends in computer technology suggests that low-cost general-purpose computers will be available in the very near future that are fast enough to meet the speed requirements of many industrial inspection problems. If this low-cost computing power is to be effectively utilized on industrial inspection problems, more general-purpose vision systems must be developed, vision systems that can be easily adapted to a variety of applications. Unfortunately, little research has gone into creating such general-purpose industrial inspection systems.

In this dissertation, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify "defects" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning of knowledge that allows concurrent parallel processing during recognition.

Based on the proposed vision system framework, a computer vision system for automated lumber grading has been developed. The purpose of this vision system is to locate and identify grading defects on rough hardwood lumber in a species independent manner. This problem seems to represent one of the more difficult and complex web inspection problems. The system has been tested on approximately 100 boards from several different species.

Three different methods for performing label verification were tested and compared. These are a rule-based approach, a k-nearest neighbor approach, and a neural network approach. The results of these tests together with other considerations suggest that the neural network approach is the better choice and hence is the one selected for use in the vision system framework.

Also, a new back-propagation learning algorithm using a steep activation function was developed that is much faster and more stable than the standard back-propagation learning algorithm. This algorithm was designed to speed the learning process involved in training a neural network to do label verification. However this algorithm seems to have general applicability.