@online{Robo,
    title     = "Roboflow (Version 1.0) [Software]",
    author    = "Dwyer, B. and Nelson, J. and Solawetz, J. et al.",
    url       = "https://roboflow.com"
}

@online{rdmo,
    title     = "Research Data Management Organiser",
    author    = "",
    url       = "https://rdmorganiser.github.io/"
}

@article{DEGIORGIO2023620,
title = {Systematic review of class imbalance problems in manufacturing},
journal = {Journal of Manufacturing Systems},
volume = {71},
pages = {620-644},
year = {2023},
issn = {0278-6125},
doi = {https://doi.org/10.1016/j.jmsy.2023.10.014},
url = {https://www.sciencedirect.com/science/article/pii/S0278612523002157},
author = {Andrea {de Giorgio} and Gabriele Cola and Lihui Wang},
keywords = {Manufacturing, Class imbalance, Data manipulation, Machine learning, Deep learning},
abstract = {Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the data modeling of many of the real-world processes that are being digitized. The manufacturing industry turns out to be highly affected by this problem, especially in fault inspection, prediction or monitoring processes, and in all those processes where the production efficiency is high and the data samples of anomalous events are rare. In this work, we systematically review all the data manipulation, machine learning or deep learning solutions to the CI problem in the manufacturing domain. We also critically evaluate all the different metrics that researchers can compare in order to estimate the improvements carried by their proposed solutions, and we look at the availability of public source code and data-imbalanced datasets that can be used for benchmarking. Finally, we summarize the most applied solutions to the CI problem in manufacturing and we look at future challenges. While posing a reference for the best practices at the time of this review, we challenge researchers to standardize the use of data science algorithms for CI in the manufacturing domain.}
}

@article{KONDA,
title = {A systematic literature review on prototyping with Arduino: Applications, challenges, advantages, and limitations},
doi = {https://doi.org/10.1016/j.cosrev.2021.100364},
author = {Hari Kishan Kondaveeti and Nandeesh Kumar Kumaravelu and Sunny Dayal Vanambathina and Sudha Ellison Mathe and Suseela Vappangi},
keywords = {Raspberry Pi, BeagleBone, Sharks Cove, Waspmote}
}


@article{einstein,
  author =       "Albert Einstein",
  title =        "{Zur Elektrodynamik bewegter K{\"o}rper}. ({German})
                 [{On} the electrodynamics of moving bodies]",
  journal =      "Annalen der Physik",
  volume =       "322",
  number =       "10",
  pages =        "891--921",
  year =         "1905",
  DOI =          "http://dx.doi.org/10.1002/andp.19053221004",
  keywords =     "physics"
}

@book{dirac,
  title={The Principles of Quantum Mechanics},
  author={Paul Adrien Maurice Dirac},
  isbn={9780198520115},
  series={International series of monographs on physics},
  year={1981},
  publisher={Clarendon Press},
  keywords = {physics}
}

@book{latexcompanion,
    author    = "Michel Goossens and Frank Mittelbach and Alexander Samarin",
    title     = "The \LaTeX\ Companion",
    year      = "1993",
    publisher = "Addison-Wesley",
    address   = "Reading, Massachusetts",
    keywords  = "latex"
}
 
@online{knuthwebsite,
    author    = "Donald Knuth",
    title     = "Knuth: Computers and Typesetting",
    url       = "http://www-cs-faculty.stanford.edu/~uno/abcde.html",
    keywords  = "latex,knuth"
}

@inbook{knuth-fa,
   author = "Donald E. Knuth",
   title = "Fundamental Algorithms",
   publisher = "Addison-Wesley",
   year = "1973",
   chapter = "1.2",
   keywords  = "knuth,programming"
}

@book{knuth-acp,
   author = "Donald E. Knuth",
   publisher = "Addison-Wesley",
   title = "The Art of Computer Programming",
   series = "Four volumes",
   year = "1968",
   note = "Seven volumes planned",
   keywords  = "knuth,programming"
}

@article{ctan,
    author  = "George D. Greenwade",
    title   = "The {C}omprehensive {T}ex {A}rchive {N}etwork ({CTAN})",
    year    = "1993",
    journal = "TUGBoat",
    volume  = "14",
    number  = "3",
    pages   = "342--351",
    keywords  = "latex"
}

@online{mdwebpageki,
    author    = "Martin Lundborg, Christian Märkel",
    title     = "Künstliche Intelligenz im  Mittelstand: Relevant, Anwendungen, Transfer",
    url       = "https://www.mittelstand-digital.de/MD/Redaktion/DE/Publikationen/kuenstliche-intelligenz-im-mittelstand.pdf?__blob=publicationFile&v=5",
    keywords  = ""
}


@article{bauer,
author = {Bauer, Markus and van Dinther, Clemens and Kiefer, Daniel},
title = {Machine Learning in SME: An Empirical Study on Enablers and Success Factors},
year = {2020},
}

@article{ULRICH20212152,
title = {Relevance and Adoption of AI technologies in German SMEs – Results from Survey-Based Research},
journal = {Procedia Computer Science},
volume = {192},
pages = {2152-2159},
year = {2021},
note = {Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2021.08.228},
url = {https://www.sciencedirect.com/science/article/pii/S1877050921017245},
author = {Patrick Ulrich and Vanessa Frank},
keywords = {Artificial intelligence, machine learning, empirical study, Germany, SMEs}
}

@article{MALBURG2021581,
title = {Object Detection for Smart Factory Processes by Machine Learning},
journal = {Procedia Computer Science},
volume = {184},
pages = {581-588},
year = {2021},
note = {The 12th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 4th International Conference on Emerging Data and Industry 4.0 (EDI40) / Affiliated Workshops},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2021.04.009},
url = {https://www.sciencedirect.com/science/article/pii/S1877050921007821},
author = {Lukas Malburg and Manfred-Peter Rieder and Ronny Seiger and Patrick Klein and Ralph Bergmann},
keywords = {Process Monitoring, Object Detection, Computer Vision, Machine Learning, Industry 4.0, Cyber-Physical Production Systems}
}

@book{rudorfer,
    author    = "Martin Rudorfer",
    title     = "Towards Robust Object Detection and Pose Estimation as a Service for Manufacturing lndustries",
    year      = "2021",
    publisher = "Fraunhofer IRB Verlag"
}

@article{10.1162/dint_a_00050,
    author = {van Vlijmen, Herman and Mons, Albert and Waalkens, Arne and Franke, Wouter and Baak, Arie and Ruiter, Gerbrand and Kirkpatrick, Christine and da Silva Santos, Luiz Olavo Bonino and Meerman, Bert and Jellema, Renger and Arts, Derk and Kersloot, Martijn and Knijnenburg, Sebastiaan and Lusher, Scott and Verbeeck, Rudi and Neefs, Jean-Marc},
    title = "{The Need of Industry to Go FAIR}",
    journal = {Data Intelligence},
    volume = {2},
    number = {1-2},
    pages = {276-284},
    year = {2020},
    month = {01},
    issn = {2641-435X},
    doi = {10.1162/dint_a_00050},
    url = {https://doi.org/10.1162/dint\_a\_00050}
}


@online{Gribulis,
    author    = "Mantas Gribulis",
    title     = "Synthetic Lego brick dataset for object detection",
    url       = "https://www.kaggle.com/datasets/mantasgr/synthetic-lego-brick-dataset-for-object-detection/"
}

@online{Dreamfactor,
    author    = "DREAMFACTOR",
    title     = "Largest LEGO Dataset (600 parts)",
    url       = "https://www.kaggle.com/datasets/dreamfactor/biggest-lego-dataset-600-parts/data"
}

@online{Training,
    author    = "TRAINING DATA",
    title     = "Safety Helmet Detection Dataset",
    url       = "https://www.kaggle.com/datasets/trainingdatapro/helmet-detection/"
}

@online{FUJI,
    author    = "",
    title     = "F-UJI",
    url       = "https://www.f-uji.net/?action=test"
}

@online{TubeData1,
    author    = "TubeData1",
    title     = "Tube Object Detection Image Dataset",
    url       = "https://universe.roboflow.com/tube/tube-object-detection/dataset/1"
}

@online{Ruben,
    author    = "Ruben",
    title     = "Aughmanity_V2.0 Computer Vision Project",
    url       = "https://universe.roboflow.com/ruben-8bqya/aughmanity_v2.0"
}

@online{Zheng,
    author    = "Jianfeng Zheng, Hang Wu, Han Zhanget",
    title     = "Insulator-Defect Detection Dataset",
    url       = "https://datasetninja.com/insulator-defect-detection"
}

@online{Ding,
    author    = "Runwei Ding, Linhui Dai, Guangpeng Li",
    title     = "Augmented PCB Defect Dataset",
    url       = "https://datasetninja.com/augmented-pcb-defect"
}

%from shengjie
@software{yolov5,
  title = {Ultralytics YOLOv5},
  author = {Glenn Jocher},
  year = {2020},
  version = {7.0},
  license = {AGPL-3.0},
  url = {https://github.com/ultralytics/yolov5},
  doi = {10.5281/zenodo.3908559},
  orcid = {0000-0001-5950-6979}
}


@Article{fire6080313,
AUTHOR = {Xie, Xingang and Chen, Ke and Guo, Yiran and Tan, Botao and Chen, Lumeng and Huang, Min},
TITLE = {A Flame-Detection Algorithm Using the Improved YOLOv5},
JOURNAL = {Fire},
VOLUME = {6},
YEAR = {2023},
NUMBER = {8},
ARTICLE-NUMBER = {313},
URL = {https://www.mdpi.com/2571-6255/6/8/313},
ISSN = {2571-6255}
}

@inproceedings{NIPS2012_c399862d,
 author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {ImageNet Classification with Deep Convolutional Neural Networks},
 url = {https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf},
 volume = {25},
 year = {2012}
}


@InProceedings{Rezatofighi_2019_CVPR,
author = {Rezatofighi, Hamid and Tsoi, Nathan and Gwak, JunYoung and Sadeghian, Amir and Reid, Ian and Savarese, Silvio},
title = {Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

