On the potential of current CNN cameras for industrial surface inspection

Abstract

An important issue in industrial quality control is the inspection of rapidly moving surfaces for small defects such as scratches, dents, grooves, or chatter marks. This paper investigates the potential of the EyeRIS 1.3 camera as a state-of-the-art camera based on “cellular neural networks” (CNN) for this application in comparison to conventional image processing systems. Based on experimental data from an aluminum wire drawing process where defects with a lateral size of 100 μm have to be detected at feeding rates of 10 m/s, the potential specifications for other surface inspection applications are estimated. Using the relation between the lateral defect size and the feeding rate as a figure of merit, the CNN based system outperforms conventional image processing systems by an order or magnitude in this particular application. In general, the lighting system limits the performance at lower defect sizes and the computational power at larger defect sizes and fields of view.