Neural network based fault detection on painted surface

Machine vision systems combined with classification algorithms are being increasingly used for different applications in the age of automation. One such application would be the quality control of the painted automobile parts. The fundamental elements of the machine vision system include camera, illumination, image acquisition software and computer vision algorithms. Traditional way of thinking puts too much importance on camera systems and ignores other elements while designing a machine vision system. In this thesis work, it is shown that selecting an appropriate illumination for illuminating the surface being examined is equally important in case of machine vision system for examining specular surface. Knowledge about the nature of the surface, type and properties of the defect to be detected and classified are important factors while choosing the illumination system for the machine vision system. The main illumination system tested were bright field, dark field and structured illumination and out of the three, dark field and structured illumination gave best results. This thesis work proposes a dark field illumination based machine vision system for fault detection on specular painted surface. A single layer Artificial Neural Network model is employed for the classification of defects in intensity images of painted surface acquired with this machine vision system. The results of this research work proved that the quality of the images and size of data set used for training the Neural Network model play a vital role in the performance of the classifier algorithm