Defect Detection in Transparent Strip Material

Deep Learning Tool opens up New Possibilities

Classic image processing quickly reaches its limits, especially in the area of ​​surface inspection. Despite many options for image preprocessing and image filtering, some defects are difficult to separate from the background, which often results in high levels of pseudo rejects or undetected defects.

With the help of the Neuralzye® software from senswork, an AI inspection of transparent strip material has now been implemented, with which the detection of bubbles and inclusions can be detected. With the additional use of the so-called transmitted light deflectometry, the imperfections in the material can be made visible in the first place.

Diffraction effects through strip-shaped transmitted light illumination generate a signal in the line camera image at the point of material damage that is easily visible to the human eye. Recognition on the basis of standard algorithms is difficult, however, since the background of the image corresponds to a sinusoidal pattern. This shows the strength of Neuralyze: Based on this image information, Neuralyze detects bubbles, holes and other defects in the material with a very high level of reliability.