  In this paper, a novel optical inspection system is presented that is
directly suitable for Industry 4.0 and the implementation on IoT-devices
controlling the manufacturing process. The proposed system is capable of
distinguishing between erroneous and faultless weld seams, without explicitly
defining measurement criteria . The developed system uses a deep neural network
based classifier for the class prediction. A weld seam dataset was acquired and
labelled by an expert committee. Thereby, the visual impression and assessment
of the experts is learnt accurately. In the scope of this paper laser
triangulation images are used. Due to their special characteristics, the images
must be pre-processed to enable the use of a deep neural network. Furthermore,
two different approaches are investigated to enable an inspection of
differently sized weld seams. Both approaches yield very high classification
accuracies of up to 96.88\%, which is competitive to current state of the art
optical inspection systems. Moreover, the proposed system enables a higher
flexibility and an increased robustness towards systematic errors and
environmental conditions due to its ability to generalize. A further benefit of
the proposed system is the fast decision process enabling the usage directly
within the manufacturing line. Furthermore, standard hardware is used
throughout the whole presented work, keeping the roll-out costs for the
proposed system as low as possible.

Image and Video Processing