OBJECT DEFECT DETECTION BASED ON A VISION SYSTEM WITH A MICROCONTROLLER AND AN ARTIFICIAL NEURAL NETWORK

Abstract

Vision systems have been widely employed in industries to automate the inspection process in products. Their use provides standardized, reliable and accurate inspections when compared to a human operator. Vision systems pass to machines the ability to view and automatically extract features in order to indicate abnormalities in products. This paper proposes a vision system for capturing and preprocessing digital images, besides classifying objects with defect and objects without defect using an Artificial Neural Network model. As a case study, digital images of boxes are acquired and classified on a conveyor belt. Tests reveal that the proposed system is able to classify accurately a box with defect and a box without defect in real time. The main contribution of this paper is the proposal of a system that performs automated inspections in products, in order to detect abnormalities, and it can be easily coupled, modularly, to the existing industrial platforms.