@inproceedings{ArndtConzenElsenetal.2023,
  author    = {Arndt, Tobias and Conzen, Max and Elsen, Ingo and Ferrein, Alexander and Galla, Oskar and K{\"o}se, Hakan and Schiffer, Stefan and Tschesche, Matteo},
  title     = {Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers},
  series = {PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments},
  booktitle = {PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments},
  publisher = {ACM},
  isbn      = {9798400700699},
  doi       = {10.1145/3594806.3596558},
  pages     = {535 -- 542},
  year      = {2023},
  abstract  = {This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100\% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.},
  language  = {en}
}