웹 빅데이터를 활용한 앙상블 딥러닝 기반 불법 복제품 판독 자동화 시스템

  • 이찬재 (넷코아테크 미래기술전략연구실) ;
  • 정성호 (넷코아테크 미래기술전략연구실) ;
  • 윤영 (홍익대학교)
  • Published : 2022.03.30

Abstract

Keywords

References

  1. Organisation for Economic Co-operation and Development, & Kazimierczak, M. (2016). Trade in Counterfeit and Pirated Goods: Mapping the Economic Impact. OECD Publishing.
  2. Kim, J. G., Seo J. Y., Lee C. J., Jo S. M., Kim S. M. , Yoon S. M. & Yoon Y.. (2022). Detecting Design Infringement Using Multi-Modal Visual Data and Auto Encoder based on Convolutional Neural Network. Journal of Computer Science and Engineering, 49(2), (137-144).
  3. Dai, Z., Cai, B., Lin, Y., & Chen, J. (2021). Up-detr: Unsupervised pre-training for object detection with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1601-1610).
  4. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
  5. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  6. Kumar, S. N., Singal, G., Sirikonda, S., & Nethravathi, R. (2020, December). A novel approach for detection of counterfeit Indian currency notes using deep convolutional neural network. In IOP Conference Series: Materials Science and Engineering (Vol. 981, No. 2, p. 022018). IOP Publishing. https://doi.org/10.1088/1757-899X/981/2/022018
  7. Lee, S. H., & Lee, H. Y. (2018). Counterfeit bill detection algorithm using deep learning. Int. J. Appl. Eng. Res, 13, 304-310.
  8. Daoud, E., Vu, D., Nguyen, H., & Gaedke, M. (2020). ENHANCING FAKE PRODUCT DETECTION USING DEEP LEARNING OBJECT DETECTION MODELS. IADIS International Journal on Computer Science & Information Systems, 15(1).
  9. https://plus.kipris.or.kr/
  10. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020, August). End-to-end object detection with transformers. In European conference on computer vision (pp. 213-229). Springer, Cham.
  11. Zhang, C., & Ma, Y. (Eds.). (2012). Ensemble machine learning: methods and applications. Springer Science & Business Media.
  12. Zhou, Z. H. (2012). Ensemble methods: foundations and algorithms. CRC press.