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
