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

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 in the image data is reduced to the calculation of greyvalue histograms on a 10 x 10 pixel window. By using only eight greyvalue 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 per cent 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 per cent