@article{malburg_objectDetection_2021,
  author    = {Lukas Malburg and Manfred-Peter Rieder and Ronny Seiger and Patrick Klein and Ralph Bergmann},
  title     = {{Object Detection for Smart Factory Processes by Machine Learning}},
  booktitle = {The 4th International Conference on Emerging Data and Industry 4.0 (EDI40), Warsaw, Poland, March 23 - 26, 2021},
  journal    = {Procedia Computer Science},
  volume    = {184},
  pages     = {581--588},
  publisher = {Elsevier.},
  keywords = {{Process Monitoring, Object Detection, Computer Vision, Machine Learning, Industry 4.0, Cyber-Physical Production Systems}},
  year      = {2021},
  url       = {https://doi.org/10.1016/j.procs.2021.04.009},
  doi       = {10.1016/j.procs.2021.04.009},
  abstract={The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.},
  url = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ObjectDetectionInSmartFactories.pdf},
  note = {Best Paper.}
}

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In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.","note":"Best Paper.","bibtex":"@article{malburg_objectDetection_2021,\n author = {Lukas Malburg and Manfred-Peter Rieder and Ronny Seiger and Patrick Klein and Ralph Bergmann},\n title = {{Object Detection for Smart Factory Processes by Machine Learning}},\n booktitle = {The 4th International Conference on Emerging Data and Industry 4.0 (EDI40), Warsaw, Poland, March 23 - 26, 2021},\n journal = {Procedia Computer Science},\n volume = {184},\n pages = {581--588},\n publisher = {Elsevier.},\n keywords = {{Process Monitoring, Object Detection, Computer Vision, Machine Learning, Industry 4.0, Cyber-Physical Production Systems}},\n year = {2021},\n url = {https://doi.org/10.1016/j.procs.2021.04.009},\n doi = {10.1016/j.procs.2021.04.009},\n abstract={The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.},\n url = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ObjectDetectionInSmartFactories.pdf},\n note = {Best Paper.}\n}\n\n","author_short":["Malburg, L.","Rieder, M.","Seiger, R.","Klein, P.","Bergmann, R."],"key":"malburg_objectDetection_2021","id":"malburg_objectDetection_2021","bibbaseid":"malburg-rieder-seiger-klein-bergmann-objectdetectionforsmartfactoryprocessesbymachinelearning-2021","role":"author","urls":{"Paper":"http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ObjectDetectionInSmartFactories.pdf"},"keyword":["Process Monitoring","Object Detection","Computer Vision","Machine Learning","Industry 4.0","Cyber-Physical Production Systems"],"metadata":{"authorlinks":{"bergmann, r":"https://bibbase.org/show?bib=https%3A%2F%2Fweb.wi2.uni-trier.de%2Fpublications%2FWI2Publikationen_IoT.bib&group0=year&css=https%3A%2F%2Fweb.wi2.uni-trier.de%2Fpublications%2FWI2.css","malburg, l":"https://bibbase.org/show?bib=https%3A%2F%2Fweb.wi2.uni-trier.de%2Fpublications%2FPublicationsMalburg.bib&group0=year&css=https%3A%2F%2Fweb.wi2.uni-trier.de%2Fpublications%2FWI2.css"}},"downloads":73},"bibtype":"article","biburl":"https://web.wi2.uni-trier.de/publications/WI2Publikationen_IoT.bib","creationDate":"2021-01-18T13:24:16.368Z","downloads":73,"keywords":["process monitoring","object detection","computer vision","machine learning","industry 4.0","cyber-physical production systems"],"search_terms":["object","detection","smart","factory","processes","machine","learning","malburg","rieder","seiger","klein","bergmann"],"title":"Object Detection for Smart Factory Processes by Machine Learning","year":2021,"dataSources":["HPSXnFwLpXtbkmbx7","vtdjwAo6eNiqRLfnG","CnwPa99ZchEF4SZgh","nZxfXH3fRFhwWejKL","MSp3DzP4ToPojqkFy","Td7BJ334QwxWK4vLW","J3orK6zvpR7d8vDmC"]}