Feature Extraction Network SFPN for Edge Computing Scenarios-Reference-Cited by-同舟云学术

Feature Extraction Network SFPN for Edge Computing Scenarios

Published:2023-07-14 Issue: Volume: Page:
ISSN:
Container-title:2023 6th International Conference on Signal Processing and Machine Learning (SPML)
language:
Short-container-title:

Author:

Yao Minda¹^ORCID,Liu Jueting¹^ORCID,Liu Xingyu¹^ORCID,Chen Wei²^ORCID,Xu Tingting¹^ORCID,Liu Yingchun¹^ORCID

Affiliation:

1. School of Computer Science & Technology, China University of Mining and Technology, China

2. School of Computer Science & Technology, China University of Mining and Technology, China and School of Mechanical,Electrical & Information Engineering, China University of Mining and Technology(Beijing), China

Funder

the Fundamental Research Funds for the Central Universities

the National Natural Science Foundation of China (NSFC)

the Graduate Innovation Program of China University of Mining and Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3614008.3614023

Reference10 articles.

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2. NVIDIA Jetson TX2 Platform;Smart Manufacturing,2017

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4. J. Redmon , A. Farhadi , and Ieee, "YOLO9000 : Better , Faster, Stronger," in 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Honolulu , HI , 2017 , pp. 6517 - 6525 , 2017. J. Redmon, A. Farhadi, and Ieee, "YOLO9000: Better, Faster, Stronger," in 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 6517-6525, 2017.

5. J. Redmon and A. Farhadi . ( 2018 ). YOLOv3: An Incremental Improvement. Available: https://arxiv.org/abs/1804.02767 . J. Redmon and A. Farhadi. (2018). YOLOv3: An Incremental Improvement. Available: https://arxiv.org/abs/1804.02767.