Fast detection and classification of defects on treated metal surfaces using a backpropagation neural network

Abstract

A fast classifier based on a neural network is described which is the central part of an optical inspection system. Defects on treated metal surfaces are detected and classified by textural segmentation. The main purpose of this work is the development of an optical inspection system for a wide range of real-time applications. Therefore, the preprocessing of the image data is reduced to the calculation of gray-value histograms on a 10*10 pixel window. By using only eight gray-value classes in the histograms, an efficient reduction of the data is obtained. The histograms calculated on each window are presented to a three-layered perceptron net for defect detection and classification. This method is applied to a 100% surface inspection of rolling bearing metal rings. Depending on the defect class investigated the misclassification rate of the window classifier ranged from 1.5 to 11.5%.<>