A self-training visual inspection system with a neural network classifier

A self-training visual inspection system using a connectionist classifier is presented. The system is composed of a control unit, a signal-processing unit, and a connectionist classifier. The control unit both generates the training set and performs the function of teacher to the classifier. The second unit compresses the two-dimensional image into a one-dimensional signal. Potential flaws extracted from the one-dimensional signal are sent to the classifier. The classifier used in this work is a standard multilayer connectionist neural network that uses backpropagation for learning. The system is applied to two inspection tasks involving two-dimensional surfaces characterized by a known intensity distribution. Diagnostics for evaluating the classifier are presented, along with an evaluation of the classifier´s performance.<>