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Defect images in auditing

Automatic recognition and classification

Challenge

  • Currently too much time is required to summarize the defect image (consisting of defect, type of defect and location on the vehicle) in the system
  • Goals:
    • Faster and easier capture of the defect in the system by automated image recognition and classification of the defect
    • Generating a proposal list from which the matching attributes can be selected

Solution

Image recognition by Deep Neural Networks

  • Creation and labeling of defect classes (manually)
  • Training a deep neural network
  • Generating proposals from the most likely matches
  • Interactive (not fully automated)

Benefit

  • Support of auditors in defect recognition; simplification of the recording process
  • Faster recording of the defect image in the system without time-consuming manual search for the correct component and defect descriptions
  • Uniform proposals for defect images ensure a consistent data basis

Your contact

Dragan, Sunjka

Dragan Sunjka

Lead IT Consultant Automotive & Manufacturing

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