An object perception and positioning method via deep perception learning object detection-Reference-Cited by-同舟云学术

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An object perception and positioning method via deep perception learning object detection

Published:2021-01-24 Issue: Volume: Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Xiao Limei¹,Zhang Yachao¹²,Gao Weizhe¹^ORCID,Xu Dayou¹^ORCID,Li Ce¹^ORCID

Affiliation:

1. College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China

2. School of Informatics Xiamen University Xiamen China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6203

Reference19 articles.

1. ZhangS WenL BianX LeiZ LiSZ. Single‐shot refinement neural network for object detection. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Jun 18‐22 2018 Salt Lake City UT USA: IEEE;2018:4203‐4212.

2. GirshickR DonahueJ DarrellT MalikJ. Rich feature hierarchies for accurate object detection and semantic segmentation. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Jun 23‐28 2014 Columbus OH USA: IEEE;2014:580‐587.

3. GirshickR. Fast r‐cnn. Paper presented at: Proceedings of the IEEE International Conference on Computer Vision. December 7‐13 2015 Santiago Chile: IEEE;2015:1440‐1448.

4. Faster r‐cnn: towards real‐time object detection with region proposal networks;Ren S;IEEE Trans Pattern Anal Mach Intell,2016

5. HeK GkioxariG DollárP GirshickR. Mask r‐cnn. Paper presented at: Proceedings of the IEEE International Conference on Computer Vision. October 22‐29 2017 Venice Italy: IEEE;2017:2961‐2969.

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