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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.1
- 수록면
- 170 - 180 (11page)
- DOI
- 10.9717/kmms.2024.27.1.170
이용수
초록· 키워드
오류제보하기
Recently, intelligent systems with artificial intelligence have been basically adopted in the field of CCTV, and the method of analyzing images input from CCTV using deep learning algorithms is mainly used in the server. However, image quality deterioration may occur in the process of transmitting and storing images, reliability is difficult to be guaranteed in terms of security, and continuous investment in server construction and management is required. When building a CCTV network using an SoC-based edge device equipped with NPU functions, these shortcomings can be overcome and better quality services can be provided. However, since lower performance NPU must be utilized compared to the server, various studies should be accompanied to reduce the weight of the deep learning model and improve accuracy. In this paper, we implemented a deep learning model that can guarantee high speed and accuracy by optimizing and lightweighting the Yolov5M model specialized for the NPU environment using the NAS (Neural Architecture Search) technique.
목차
ABSTRACT
1. 서론
2. 이론
3. 제안한 방법
4. 실험 결과 및 고찰
5. 결론
REFERENCE
참고문헌 (25)
참고문헌 신청
ETRI, Edge Computing Technology Trends, Electronics and Telecommunications Trends, 2020.
TTA, Intelligent Edge Computing Technology and Standardization Trends, ICT Standard & Certification, 2019.
Ultralytics, Yolov5(2020), https://github.com/ultralytics/yolov5/ (accessed October 8, 2023).
C. Wang, A. Bochkovskiy, and H.M. Liao, “YOLOv7: Trainable Bag-of-freebies Sets New State-of-the-art for Real-time Object Detectors,” arXiv Preprint, arXiv:2207.02696, 2022.
P. Ren, and X. Chang, “A Survey of Deep Active Learning,” arXiv Preprint, arXiv:2009.00236, 2021.
TTA, Intelligent Edge Computing Technology and Standardization Trends, ICT Standard & Certification, 2019.
Ultralytics, Yolov5(2020), https://github.com/ultralytics/yolov5/ (accessed October 8, 2023).
C. Wang, A. Bochkovskiy, and H.M. Liao, “YOLOv7: Trainable Bag-of-freebies Sets New State-of-the-art for Real-time Object Detectors,” arXiv Preprint, arXiv:2207.02696, 2022.
P. Ren, and X. Chang, “A Survey of Deep Active Learning,” arXiv Preprint, arXiv:2009.00236, 2021.
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