Micro-YOLO+: Searching Optimal Methods for Compressing Object Detection Model Based on Speed, Size, Cost, and Accuracy-Reference-Cited by-同舟云学术

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Micro-YOLO+: Searching Optimal Methods for Compressing Object Detection Model Based on Speed, Size, Cost, and Accuracy

Published:2022-07-23 Issue:5 Volume:3 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Hu Lining,Zhang Yuhang,Zhao Yang,Wu Tong,Li Yongfu^ORCID

Funder

National Key Research and Development Program of China

Science, Technology and Innovation Action Plan of Shanghai Municipality, China

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s42979-022-01299-3.pdf

Reference26 articles.

1. Borji A, Cheng M-M, Hou Q, Jiang H, Li J. Salient object detection: a survey. Comput Visual Media. 2019;5(1):117–50.

2. Pan M, Zhu X, Li Y, Qian J, Liu P. MRNet: a keypoint guided multi-scale reasoning network for vehicle re-identification. In: Proc. neural information processing. 2020. p. 469–78.

3. Yan W, Ji Y, Ma C, Hu L, Zhao Y, Li Y, Wang G, Lian Y. A computationally efficient, hardware re-configurable architecture for QRS detection and ECG authentication. In: IEEE Asian solid-state circuits conference (A-SSCC). 2021. p. 1–2.

4. Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proc. IEEE conference on computer vision and pattern recognition. 2014. p. 580–87.

5. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC . SSD: single shot multibox detector. In: Proc. European conference on computer vision. 2016. p. 21–37.

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