Illumination normalization for Industry 4.0

Abstract

With the introduction of machine learning algorithms in industry came a new improvement to excising production line. Image recognisance can now be used to detect defects of the product made by these production lines. However these kinds of flaw detection can be fooled by mirror-like specular light effect on these products, which can reduce the reliability of these detection methods. In this thesis two approaches are taken to
minimize these spurious specular light features. Here I look at images of welding spots taken from a car-door production line of Fiat Chrysler Automotive Italy. First a median filter is used on multiple images of welding spots, which each have a different illumination. Secondly the same images were fed into a modified algorithm developed by Antonello et al,2018 , originally used to split fore-and background of videos, to remove these spurious features. Then I tried to improve the output of this algorithm so that oversaturated pixels in the output were replaced by a combination of pixel values of the input images that were not oversaturated.
Here is found that the median filter does not satisfy the desired goal of this thesis. The algorithm however gives a good filtered output of the input images and therefore are suitable to possibly be used for computer vision applications. The improvements made to the output of the algorithm do not correct the oversaturated pixels the right way, however the method I propose here seems to have some promising results if some improvement to this method are made, mainly concerning the normalisation of these pixels.