Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering

Jie Zhang; Yueting Yang; Shaolin Hu; Ye Ke; Xin Wang

doi:doi:10.11648/j.ijdst.20230901.12

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Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering

Jie Zhang, Yueting Yang, Shaolin Hu, Ye Ke, Xin Wang

Published in International Journal on Data Science and Technology (Volume 9, Issue 1)

Received: 8 August 2023 Accepted: 1 September 2023 Published: 13 September 2023

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Abstract

Unmanned vehicles detect the traffic environment through on-board sensors, automatically identify road safety information without human control, and automatically plan parameters such as driving speed and route. However, foggy weather will reduce the detection accuracy of visibility by unmanned vehicles and affect the driving safety of unmanned vehicles. In order to reduce the probability of dangerous accidents of unmanned vehicles caused by fog and improve the unmanned vehicle driving capability in foggy environments, a fast-guided-filtering fog road visibility detection algorithm is proposed. Firstly, the original image is processed by dark channel prior, and the values of atmospheric light intensity and transmittance are calculated respectively. Secondly, the fast-guided-filtering is applied to the dark channel image to enhance the edge details of the image. The atmospheric scattering coefficient is estimated by selecting double reference points. Finally, combined with the definition of visibility, its value detection based on video image sequence is realized. The experimental results confirm that the accuracy of this method for detecting visibility on foggy roads can reach 92.3%. It can provide reliable detection data support for the subsequent driving decision of unmanned vehicles such that vehicles can reasonably plan driving speed and route and ensure driving safety with certain practicability and feasibility.

Published in	International Journal on Data Science and Technology (Volume 9, Issue 1)
DOI	10.11648/j.ijdst.20230901.12
Page(s)	13-19
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Unmanned Vehicle, Visibility Detection, Foggy Road, Dark Channel Prior, Fast-Guided-Filtering

References

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[5]	Liu Dongwei, Mu Haizhen, He Qianshan, Shi Jun, Wang Yadong, Wu Xueqin. A Low Visibility Recongnition Algorithm Based on Surveillance Video. [J]. Journal of Applied Meteorological Science, 2022, 33 (04): 501-512, DOI: 10.11898/1001-7313.20220410.
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[8]	CAI Yi, YANG Zhuomin, ZHANG Zhi, et al. Research on Visibility Calculation Method Based on Video Image [J]. Journal of Atmospheric and Environmental Optics, 2021, 16 (02): 81-87.
[9]	XuMin. Research on Visibility Detection Algorithm for Fog Image [D]. Southwest University of Science and Technology, 2018.
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[14]	WANG Shiqi, HE Ao, MA Linjun, DENG Xi, LI Yingxiang. Research on visibility observation’s algorithm based on the auxiliary lines for urban traffic roads [J]. Communication & Information Technology, 2023 (01): 105-108.
[15]	HAN Tao. Implementation of Image Enhancement Algorithm Using Fast Guided Filtering [J]. Computer & Digital Engineering, 2022, 50 (10): 2303-2306, DOI: 10.3969/j.issn.1672-9722.2022.10.033.
[16]	Lili L, Xuelian W, Yinghua L, et al. The Effect of Sea Surface Temperature on Relative Humidity and Atmospheric Visibility of a Winter Sea Fog Event over the Yellow-Bohai Sea [J]. Atmosphere, 2022, 13 (10), DOI: 10.3390/ATMOS13101718.
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APA Style

Jie Zhang, Yueting Yang, Shaolin Hu, Ye Ke, Xin Wang. (2023). Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering. International Journal on Data Science and Technology, 9(1), 13-19. https://doi.org/10.11648/j.ijdst.20230901.12

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ACS Style

Jie Zhang; Yueting Yang; Shaolin Hu; Ye Ke; Xin Wang. Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering. Int. J. Data Sci. Technol. 2023, 9(1), 13-19. doi: 10.11648/j.ijdst.20230901.12

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AMA Style

Jie Zhang, Yueting Yang, Shaolin Hu, Ye Ke, Xin Wang. Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering. Int J Data Sci Technol. 2023;9(1):13-19. doi: 10.11648/j.ijdst.20230901.12

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@article{10.11648/j.ijdst.20230901.12,
  author = {Jie Zhang and Yueting Yang and Shaolin Hu and Ye Ke and Xin Wang},
  title = {Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering},
  journal = {International Journal on Data Science and Technology},
  volume = {9},
  number = {1},
  pages = {13-19},
  doi = {10.11648/j.ijdst.20230901.12},
  url = {https://doi.org/10.11648/j.ijdst.20230901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20230901.12},
  abstract = {Unmanned vehicles detect the traffic environment through on-board sensors, automatically identify road safety information without human control, and automatically plan parameters such as driving speed and route. However, foggy weather will reduce the detection accuracy of visibility by unmanned vehicles and affect the driving safety of unmanned vehicles. In order to reduce the probability of dangerous accidents of unmanned vehicles caused by fog and improve the unmanned vehicle driving capability in foggy environments, a fast-guided-filtering fog road visibility detection algorithm is proposed. Firstly, the original image is processed by dark channel prior, and the values of atmospheric light intensity and transmittance are calculated respectively. Secondly, the fast-guided-filtering is applied to the dark channel image to enhance the edge details of the image. The atmospheric scattering coefficient is estimated by selecting double reference points. Finally, combined with the definition of visibility, its value detection based on video image sequence is realized. The experimental results confirm that the accuracy of this method for detecting visibility on foggy roads can reach 92.3%. It can provide reliable detection data support for the subsequent driving decision of unmanned vehicles such that vehicles can reasonably plan driving speed and route and ensure driving safety with certain practicability and feasibility.},
 year = {2023}
}

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TY  - JOUR
T1  - Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering
AU  - Jie Zhang
AU  - Yueting Yang
AU  - Shaolin Hu
AU  - Ye Ke
AU  - Xin Wang
Y1  - 2023/09/13
PY  - 2023
N1  - https://doi.org/10.11648/j.ijdst.20230901.12
DO  - 10.11648/j.ijdst.20230901.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 13
EP  - 19
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20230901.12
AB  - Unmanned vehicles detect the traffic environment through on-board sensors, automatically identify road safety information without human control, and automatically plan parameters such as driving speed and route. However, foggy weather will reduce the detection accuracy of visibility by unmanned vehicles and affect the driving safety of unmanned vehicles. In order to reduce the probability of dangerous accidents of unmanned vehicles caused by fog and improve the unmanned vehicle driving capability in foggy environments, a fast-guided-filtering fog road visibility detection algorithm is proposed. Firstly, the original image is processed by dark channel prior, and the values of atmospheric light intensity and transmittance are calculated respectively. Secondly, the fast-guided-filtering is applied to the dark channel image to enhance the edge details of the image. The atmospheric scattering coefficient is estimated by selecting double reference points. Finally, combined with the definition of visibility, its value detection based on video image sequence is realized. The experimental results confirm that the accuracy of this method for detecting visibility on foggy roads can reach 92.3%. It can provide reliable detection data support for the subsequent driving decision of unmanned vehicles such that vehicles can reasonably plan driving speed and route and ensure driving safety with certain practicability and feasibility.
VL  - 9
IS  - 1
ER  -

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Author Information

Jie Zhang

School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, China
Yueting Yang

School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, China
Shaolin Hu

School of Automation, Guangdong University of Petrochemical Technology, Maoming, China
Ye Ke

School of Automation, Guangdong University of Petrochemical Technology, Maoming, China
Xin Wang

School of Electronic Information Engineering, Xi’an University of Technology, Xi’an, China

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Plain Text BibTeX RIS

APA Style

Jie Zhang, Yueting Yang, Shaolin Hu, Ye Ke, Xin Wang. (2023). Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering. International Journal on Data Science and Technology, 9(1), 13-19. https://doi.org/10.11648/j.ijdst.20230901.12

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ACS Style

Jie Zhang; Yueting Yang; Shaolin Hu; Ye Ke; Xin Wang. Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering. Int. J. Data Sci. Technol. 2023, 9(1), 13-19. doi: 10.11648/j.ijdst.20230901.12

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AMA Style

Jie Zhang, Yueting Yang, Shaolin Hu, Ye Ke, Xin Wang. Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering. Int J Data Sci Technol. 2023;9(1):13-19. doi: 10.11648/j.ijdst.20230901.12

Copy | Download

@article{10.11648/j.ijdst.20230901.12,
  author = {Jie Zhang and Yueting Yang and Shaolin Hu and Ye Ke and Xin Wang},
  title = {Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering},
  journal = {International Journal on Data Science and Technology},
  volume = {9},
  number = {1},
  pages = {13-19},
  doi = {10.11648/j.ijdst.20230901.12},
  url = {https://doi.org/10.11648/j.ijdst.20230901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20230901.12},
  abstract = {Unmanned vehicles detect the traffic environment through on-board sensors, automatically identify road safety information without human control, and automatically plan parameters such as driving speed and route. However, foggy weather will reduce the detection accuracy of visibility by unmanned vehicles and affect the driving safety of unmanned vehicles. In order to reduce the probability of dangerous accidents of unmanned vehicles caused by fog and improve the unmanned vehicle driving capability in foggy environments, a fast-guided-filtering fog road visibility detection algorithm is proposed. Firstly, the original image is processed by dark channel prior, and the values of atmospheric light intensity and transmittance are calculated respectively. Secondly, the fast-guided-filtering is applied to the dark channel image to enhance the edge details of the image. The atmospheric scattering coefficient is estimated by selecting double reference points. Finally, combined with the definition of visibility, its value detection based on video image sequence is realized. The experimental results confirm that the accuracy of this method for detecting visibility on foggy roads can reach 92.3%. It can provide reliable detection data support for the subsequent driving decision of unmanned vehicles such that vehicles can reasonably plan driving speed and route and ensure driving safety with certain practicability and feasibility.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Visibility Detection of Unmanned Vehicle in Fog Based on Fast-Guided-Filtering
AU  - Jie Zhang
AU  - Yueting Yang
AU  - Shaolin Hu
AU  - Ye Ke
AU  - Xin Wang
Y1  - 2023/09/13
PY  - 2023
N1  - https://doi.org/10.11648/j.ijdst.20230901.12
DO  - 10.11648/j.ijdst.20230901.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 13
EP  - 19
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20230901.12
AB  - Unmanned vehicles detect the traffic environment through on-board sensors, automatically identify road safety information without human control, and automatically plan parameters such as driving speed and route. However, foggy weather will reduce the detection accuracy of visibility by unmanned vehicles and affect the driving safety of unmanned vehicles. In order to reduce the probability of dangerous accidents of unmanned vehicles caused by fog and improve the unmanned vehicle driving capability in foggy environments, a fast-guided-filtering fog road visibility detection algorithm is proposed. Firstly, the original image is processed by dark channel prior, and the values of atmospheric light intensity and transmittance are calculated respectively. Secondly, the fast-guided-filtering is applied to the dark channel image to enhance the edge details of the image. The atmospheric scattering coefficient is estimated by selecting double reference points. Finally, combined with the definition of visibility, its value detection based on video image sequence is realized. The experimental results confirm that the accuracy of this method for detecting visibility on foggy roads can reach 92.3%. It can provide reliable detection data support for the subsequent driving decision of unmanned vehicles such that vehicles can reasonably plan driving speed and route and ensure driving safety with certain practicability and feasibility.
VL  - 9
IS  - 1
ER  -

Copy | Download