Increasing the image recognition accuracy in machine vision systems with added noise due to technological issues

Typical application of machine vision systems in the discrete automated production is quality control, measurement or classification of moving parts, placed on conveyor belts. Different technical issues (lighting problems, vibrations near camera or conveyor belt, etc.) can lead to noisy images and to wrong classifications or faulty measurements by the vision inspection system. The correlation between motion blur noise (added by technical malfunctions) and the correct measurement by the machine vision system is examined in this paper. First part of the study is to define the influence of motion blur to visual inspection of moving parts with linear velocity of up to 25 m/min. The analyzed vision inspections are size measurement, classification, OCR and code readings. A second study is performed to derive and to propose additional image filtration or vision inspection steps to minimize the wrong measurements according to the inspection type. Of great importance is the added additional amount of processing time. This requires accurate benchmarking of the proposed algorithms within similar laboratory conditions.