Inspecting rapidly moving surfaces for small defects using CNN cameras

Abstract

A continuous increase in production speed and manufacturing precision raises a demand for the automated detection of small image features on rapidly moving surfaces. An example are wire drawing processes where kilometers of cylindrical metal surfaces moving with 10 m/s have to be inspected for defects such as scratches, dents, grooves, or chatter marks with a lateral size of 100 µm in real time. Up to now, complex eddy current systems are used for quality control instead of line cameras, because the ratio between lateral feature size and surface speed is limited by the data transport between camera and computer. This bottleneck is avoided by cellular neural network (CNN) cameras which enable image processing directly on the camera chip. This article reports results achieved with a d emonstrator based on this novel analogue camera computer system. The results show that computational speed and accuracy of the analogue computer system are sufficient to detect and discriminate the different types of defects. Area images with 176 x 144 pixels are acquired and evaluated in real time with frame rates of 4 to 10 kHz depending on the number of defects to be detected. These frame rates correspond to equivalent line rates on line cameras between 360 and 880 kHz, a number far beyond the available features. Using the relation between lateral feature size and surface speed as a figure of merit, the CNN based system outperforms conventional image processing systems by an order of magnitude

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices

“…However there are significant advantages if devices can as well be switched by optical means since optical signalling supports higher bandwidth communication, faster transmission speeds and the ability to decouple signals from electronic noise. A suite of potential new applications include; switchable memory elements in optical communications and photonic integrated circuits, optically tunable synaptic elements and new types of light sensors that can be used in cellular neural network cameras [9][10][11]. The latter having the important advantage that some in-memory processing of the image (e.g.…”