Fetching the paper…
Reading the bibliography…
Supervised object detection methods provide subpar performance when applied to Foreign Object Debris (FOD) detection because FOD could be arbitrary objects according to the Federal Aviation Administration (FAA) specification.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science , vol. 313, no. 5786, pp. 504–507, 2006. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1127647
2006
Earlier work this paper cites.
Federal Aviation Administration, “Airport foreign object debris management,” Federal Aviation Administration, Tech. Rep., 09 2010, advisory circular: 150/5210-24
2010
Earlier work this paper cites.
M. Alexander-Adams, “Fact sheet – foreign object debris (fod),” https://fodprevention.com/fact-sheet-foreign-object-debris-fod/ , Nov 2013
2013
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 , N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 234–241
2015
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in Computer Vision – ECCV 2016 , B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing, 2016, pp. 21–37
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
2018
Cited alongside, same era.
W. L. Lee and O. Yakimenko, “Feasibility assessment of suas-based automated fod detection system,” in 2018 International Conference on Control and Robots (ICCR) , 2018, pp. 89–97
2018
Cited alongside, same era.
P. Li and H. Li, “Research on fod detection for airport runway based on yolov3,” in 2020 39th Chinese Control Conference (CCC) , 2020, pp. 7096–7099
2020
Cited alongside, same era.
T. Munyer, D. Brinkman, C. Huang, and X. Zhong, “Integrative use of computer vision and unmanned aircraft technologies in public inspection: Foreign object debris image collection,” in DG.O2021: The 22nd Annual International Conference on Digital Government Research , ser. DG.O’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 437–443. [Online]. Available: https://doi.org/10.1145/3463677.3463743
2021
Later among the works it cites.
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 6877–6886
2021
Later among the works it cites.
R. Atienza, “Vision transformer for fast and efficient scene text recognition,” in Document Analysis and Recognition – ICDAR 2021 , J. Lladós, D. Lopresti, and S. Uchida, Eds. Cham: Springer International Publishing, 2021, pp. 319–334
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=YicbFdNTTy
2021
Cited alongside, same era.
2021
Later among the works it cites.
“Computer Vision Annotation Tool,” https://github.com/openvinotoolkit/cvat, 2021, last accessed on July 1
2021
Later among the works it cites.