과제정보
본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터지원사업 (IITP-2022-2020-0-01808*)의 연구결과로 수행되었으며, 또한 정부 (과학기술정보통신부)의 재원으로 한국연구재단 (2020R1C1C100742311)의 지원을 받아 수행된 연구임.
참고문헌
- P. Kim, D. B. Im, "Understanding Hybrid Governance: The Case of Waste Recycle Management," The Korean Governance Review, Vol. 27, No. 2, pp. 29-56, 2020 (in Korean). https://doi.org/10.17089/KGR.2020.27.2.002
- J. Redmon, S. K. Divvala, R. B. Girshick, A. Farhadi, "You only look once: Unified, Real-time Object Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
- S. Ren, K. He, R. B. Girshick, J. Sun, "Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks," Advances in Neural Information Processing Systems, Vol. 28, 2015.
- K. He, G. Gkioxari, P. Dollar, R. B. Girshick, "Mask R-CNN, " In Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969, 2017.
- Ultralytics, "YOLOv5," https://github.com/ultralytics/yolov5, 2022.
- A. Bochkovskiy, C. Wang, H. M. Liao, "Yolov4: Optimal Speed and Accuracy of Object Detection," arXiv preprint, arXiv:2004.10934, 2020.
- H. J. Lee, H. C. Jung, "Implementation of Recyclable Trash Object Detection Model Based on YOLOv5," In Proceedings of the Institute of Electronics and Information Engineers, pp. 1066-1069, 2021 (in Korean).
- T. Devries, G. W. Taylor, "Improved Regularization of Convolutional Neural Networks with Cutout," arXiv preprint, arXiv:1708.04552, 2017.
- G. Kang, X. Dong, L. Zheng, Y. Yang, "Patchshuffle Regularization, " arXiv preprint, arXiv:1707.07103, 2017.
- H. Zhang, M. Cisse, Y. N. Dauphin, D. Lopez-Paz, "Mixup: Beyond Empirical risk Minimization," arXiv preprint, arXiv:1710.09412, 2017.
- S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, Y. Yoo, "Cutmix: Regularization Strategy to Train Strong Classifiers with Localizable Features," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023-6032, 2019.
- G. Ghiasi, Y. Cui, A. Srinivas, R. Qian, T. Lin, E. D. Cubuk, Q. V. Le, B. Zoph, "Simple Copy-paste is a Strong Data Augmentation Method for Instance Segmentation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918-2928, 2021.
- N. Bodla, B. Singh, R. Chellappa, L. S. Davis, "Improving Object Detection with one line of code," arXiv preprint, arXiv:1704.04503, 2017.
- H. Rezatofighi, N. Tsoi, J. Y. Gwak, A. Sadeghian, I. Reid, S. Savarese, "Generalized Intersection Over Union: A Metric and A loss for Bounding box Regression," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658-666, 2019.
- Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, D. Ren, "Distance-iou Loss: Faster and Better Learning for Bounding box Regression," In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 07, pp. 12993-13000, 2020.
- IITP, "2020 AI Grand Challenge," https://ai-challenge.kr, 2020.
- M. Everingham, L. Gool, C. K. Williams, J. Winn, A. Zisserman, "The Pascal Visual Object Classes (voc) Challenge," International Journal of Computer Vision, Vol. 88, No. 2, pp. 303-338, 2010. https://doi.org/10.1007/s11263-009-0275-4
- Ultralytics, "YOLOv5 pre-trained weights of COCO dat aset," https://github.com/ultralytics/yolov5/releases/tag/v6.1, 2022.