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Automated Dataset Amplification and its Application to Small Dataset Object Detection Transfer Learning

Published:25 September 2021Publication History
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  1. M. Vashisht and B. Kumar, "A Survey Paper on Object Detection Methods in Image Processing," 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 2020, pp. 1-4, doi: 10.1109/ICCSEA49143.2020.9132871.Google ScholarGoogle Scholar
  2. J. Deng, W. Dong, R. Socher, L. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848.Google ScholarGoogle Scholar
  3. J. Redmon and A. Farhadi, “YoloV3: An Incremental Improvement”, Arxiv.org, Computer Vision and Pattern Recognition, 2018.Google ScholarGoogle Scholar
  4. Cheng Lei, Benlin Hu, Dong Wang, Shu Zhang, and Zhenyu Chen. 2019. A Preliminary Study on Data Augmentation of Deep Learning for Image Classification. In Proceedings of the 11th Asia-Pacific Symposium on Internetware (Internetware '19). Association for Computing Machinery, New York, NY, USA, Article 20, 1–6. DOI:https://doi.org/10.1145/3361242.3361259Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. Dalal and T. Moh, "Fine-Grained Object Detection Using Transfer Learning and Data Augmentation," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, 2018, pp. 893-896, doi: 10.1109/ASONAM.2018.8508293.Google ScholarGoogle Scholar
  6. Y. Bo , "Helmet Detection under the Power Construction Scene Based on Image Analysis," 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, 2019, pp. 67-71, doi: 10.1109/ICCSNT47585.2019.8962495.Google ScholarGoogle Scholar
  7. K. Zhang, Z. Cao and J. Wu, "Circular Shift: An Effective Data Augmentation Method For Convolutional Neural Network On Image Classification," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 1676-1680, doi: 10.1109/ICIP40778.2020.9191303.Google ScholarGoogle Scholar
  8. H. Shin, K. Lee and C. Lee, "Data Augmentation Method of Object Detection for Deep Learning in Maritime Image," 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea (South), 2020, pp. 463-466, doi: 10.1109/BigComp48618.2020.00-25.Google ScholarGoogle Scholar
  9. Y. Luo and L. Zhu, "Research on Data Augmentation for Object Detection Based on X- ray Security Inspection Picture," 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA), Dalian, China, 2020, pp. 219-222, doi: 10.1109/AEECA49918.2020.9213654.Google ScholarGoogle Scholar
  10. M. Olafenwa and J. Olafenwa, “ImageAI, an open source python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities”, https://github.com/OlafenwaMoses/ImageAI, March, 2018.Google ScholarGoogle Scholar
  11. M. R. Abid, F. Shi and E. M. Petriu, "Dynamic hand gesture recognition from Bag-of-Features and local part model," 2012 IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE 2012) Proceedings, Munich, Germany, 2012, pp. 78-82, doi: 10.1109/HAVE.2012.6374443.Google ScholarGoogle Scholar
  12. M. R. Abid, L. B. Santiago Melo and E. M. Petriu, "Dynamic sign language and voice recognition for smart home interactive application," 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Gatineau, QC, Canada, 2013, pp. 139-144, doi: 10.1109/MeMeA.2013.6549723.Google ScholarGoogle Scholar
  13. M. R. Abid, E. M. Petriu and E. Amjadian, "Dynamic Sign Language Recognition for Smart Home Interactive Application Using Stochastic Linear Formal Grammar," in IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 3, pp. 596-605, March 2015, doi: 10.1109/TIM.2014.2351331.Google ScholarGoogle ScholarCross RefCross Ref
  14. M. R. Abid, P. E. Meszaros, R. F. d. Silva and E. M. Petriu, "Dynamic hand gesture recognition for human-robot and inter-robot communication," 2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Ottawa, ON, Canada, 2014, pp. 12-17, doi: 10.1109/CIVEMSA.2014.6841431.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    ICISDM '21: Proceedings of the 2021 5th International Conference on Information System and Data Mining
    May 2021
    162 pages
    ISBN:9781450389549
    DOI:10.1145/3471287

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    Publication History

    • Published: 25 September 2021

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