Vision based, statistical learning system for fault recognition in industrial assembly environment

Abstract

The paper presents a statistical learning system based visual solution developed and applied for fault detection in industrial environment. As a mobile vision system the area of use was the automatic detection of rare faults in complex assembled objects. The object detection, the fore- and background separation, and the multi-model database enables the system to manage irregular batches of the different objects. A multi-model database guarantees that the object is compared with the statistically most relevant model, therefore it reduces the number of false alarms. The developed system is able to detect faults with the size of 2% of the total picture based on previously learned models.