Camera-based damage detection for quality control in automobiles
Implemented a proof-of-concept in the quality control of finished vehicles by analyzing images of the vehicle:
- Agile control and project implementation
- Analyzed and evaluated the first heterogeneous data supply (different exposure, small number of images, different positions, etc.) for suitability in the development of a neural network
- Extensive data engineering to absorb the limited quality of data: Background isolation, neutralization of different exposures through edge formation, formation of similar clusters with respect to exposure and vehicle type, etc.
- Successful development of a neural network for dirt and scratch detection in vehicles
- Recommendations for action and process optimization for a future operational implementation
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Industry competence Automotive
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