Automated Recognition of Mechanical Parts with Machine Vision System

Abstract

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear, and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of three objects are used as an input. The Fourier descriptor technique is used for recognition of objects.  Artificial Neural Network (ANN) is used for classification of three different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.