Quality control of finished vehicles through the analysis of vehicle images

Automotive OEM

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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