An improved algorithm based on deep learning network for road image redundancy removal-Reference-Cited by-同舟云学术

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An improved algorithm based on deep learning network for road image redundancy removal

Published:2022-01-24 Issue:8 Volume:78 Page:10385-10404
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Yang Shengli,Wang Haoliang,Chen Qiang

Funder

GDAS’ Project of Building a World-class Research Institution in China

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-021-04302-5.pdf

Reference33 articles.

1. Fan J, Yang Z (2020) Deep residual network based road detection algorithm for remote sensing images. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020, pp 1723–1726. https://doi.org/10.1109/ICMCCE51767.2020.00378

2. Wadhawan A, Kumar P (2021) Sign language recognition systems: a decade systematic literature review. Arch Comput Methods Eng 28:785–813. https://doi.org/10.1007/s11831-019-09384-2

3. Laddha A, Kocamaz MK, Navarro-Serment LE, Hebert M (2016) Map-supervised road detection. In: 2016 IEEE intelligent vehicles symposium (IV), pp 118–123. https://doi.org/10.1109/IVS.2016.7535374

4. Wang Q, Fang J, Yuan Y (2015) Adaptive road detection via context-aware label transfer. Neurocomputing 158(22):174–183

5. Yuan Y, Jiang Z, Wang Q (2015) Video-based road detection via online structural learning. Neurocomputing 168(30):336–347

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