Neural network painting defect classification using Karhunen–Loeve transformation

This paper deals with the problem of painting defect detection on reflecting surface objects. The problem has been approached with an optical inspecting method. A laser beam hits the object surface. The light scattered from the rough surface generates a digital speckle. The speckle is affected by the painting defect. Using the Karhunen–Loeve transformation, the speckle pattern is transformed into a feature vector. This information is used to train the neural-networks in recovering the defect. The reliability and effectiveness of a prototype is validated by experimental results. At the end, the proposed method is compared with another optical inspection method.