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Defect images in auditing

Automatic recognition and classification


    • 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


      • 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)


    • 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 msg

Dragan Sunjka
Lead IT Consultant
Automotive & Manufacturing