Research on highway rain monitoring based on rain monitoring coefficient.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
23 Feb 2024
23 Feb 2024
Historique:
received:
26
03
2023
accepted:
31
01
2024
medline:
24
2
2024
pubmed:
24
2
2024
entrez:
23
2
2024
Statut:
epublish
Résumé
The real-time and accurate monitoring of severe weather is the key to reducing traffic accidents on highways. Currently, rainy day monitoring based on video images focuses on removing the impact of rain. This article aims to build a monitoring model for rainy days and rainfall intensity to achieve precise monitoring of rainy days on highways. This paper introduces an algorithm that combines the frequency domain and spatial domain, thresholding, and morphology. It incorporates high-pass filtering, full-domain value segmentation, the OTSU method (the maximum inter-class difference method), mask processing, and morphological opening for denoising. The algorithm is designed to build the rain coefficient model P
Identifiants
pubmed: 38396045
doi: 10.1038/s41598-024-53360-1
pii: 10.1038/s41598-024-53360-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4470Subventions
Organisme : Natural Foundation of Shandong Province
ID : ZR2022MG077
Organisme : Project of Jinan Municipal Bureau of science and Technology
ID : 2019GXRC022
Organisme : The research is partially supported by Jinan City's Self-Developed Innovative Team Project for Higher Educational Institutions
ID : 20233040
Informations de copyright
© 2024. The Author(s).
Références
Gunawan, A. A. S. et al. Inferring the level of visibility from hazy image. J. Int. J. Bus. Intell. Data Min. 16(2), 177–189 (2020).
Tang, W. et al. A Method for measuring visibility under foggy weather for expressways based on Siamese network. J. Traffic Inf. Secur. 41(4), 122–131 (2023).
Ismail, M. K. & Al-Ameen, Z. Adapted single scale retinex algorithm for nighttime image enhancement. J. AL-Rafidain J. Comput. Sci. Math. 16(1), 59–69 (2022).
Zhou, I. C. et al. Multi-scale retinex-based adaptive gray-scale transformation method for under water image enhancement. J. Multimed. Tools Appl. 81(2), 1811–1831 (2022).
doi: 10.1007/s11042-021-11327-8
Elhashemi, A. et al. Real-time snow detection based on machine vision and vehicle kinematics: A nonparametric data fusion analysis protocol. J. Saf. Res. 83, 163–180 (2022).
doi: 10.1016/j.jsr.2022.08.013
Quan, Y. Y. et al. Image snow removal by deep reversible separation. J. IEEE Trans. Circuits Syst. Video Technol. 33(7), 3133–3144 (2023).
doi: 10.1109/TCSVT.2022.3233655
Chiu, S. T. et al. Sequentially environment-aware and recursive multiscene image enhancement for IoT-enabled smart services. J. IEEE Syst. J. 16(4), 6130–6141 (2022).
doi: 10.1109/JSYST.2022.3179967
Lv, C. M. et al. Research on road weather recognition method based on road segmentation. J. Highw. Transp. Technol. 40(5), 184–192 (2023).
Barnum, P. C., Narasimhan, S. & Kanade, T. Analysis of rain and snow in frequency space. J. Int. J. Comput. Vis. 86, 2–3 (2010).
Jin, X., Chen, Z. B. & Li, W. P. Ai-Gan: Asynchronous interactive generative adversarial network for single image rain removal. J. Pattern Recognit. 100, 107–143 (2020).
doi: 10.1016/j.patcog.2019.107143
Kang, L. W., Lin, C. W. & Fu, Y. H. Automatic single-frame-based rain streak removal via image decomposition. J. IEEE Trans. Image Process. A Publ. IEEE Signal. Process. Soc. 21, 4 (2012).
Hu, C. & Wang, H. W. Enhanced driving in rainy weather: Deep deployment network for single image rain removal using PGD algorithm. J. IEEE Access 11, 2169–3536 (2023).
Thatikonda, R. & Kodali, P. DeTformer: A novel efficient transformer framework for image deraining. J. Circuits Syst. Signal Process. 66(1), 23 (2023).
Fu, X. Y. & Xiao, J. Continuous image rain removal was performed using a hypermap convolutional network. J. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 9534–9551 (2023).
doi: 10.1109/TPAMI.2023.3241756
Ji, S. X., Yuan, M. X., Wu, Z. F., Jiang, Y. F. & Wang, Q. A visual segmentation algorithm for submarine wreckage incorporating linear OTSU and mathematical morphology. J. Image Process. Technol. 39(12), 101–104 (2020).
Zhang, Z. H., Jia, Q. M. & Ji, K. Research on subway tunnel crack identification method based on improved method. J. Chongqing Jiaotong Univ. Nat. Sci. Ed. 41(1), 84–90 (2022).
Deng, Z. Q., Wang, Y., Zhang, B. & Yang, C. Research on pitaya image segmentation based on Otsu algorithm and morphology. J. Intell. Comput. Appl. 12(6), 106–115 (2022).
Yu, C. G. & Liu, K. A method for navel orange recognition based on wavelet transform and Otsu threshold denoising. J. South China Agric. Univ. 41(5), 109–114 (2020).
Zeng, X. F. et al. 2022 Image recognition method of agricultural pests based on multisensor image fusion technology. Adv. Multimed. 6, 66 (2022).
Mousania, Y. et al. Optical remote sensing, brightness preserving and contrast enhancement of medical images using histogram equalization with minimum cross-entropy-Otsu algorithm. J. Opt. Quantum Electron. 6, 66 (2022).
Zhang, J., Lai, Z. L. & Sun, J. Method, area growth method and morphology combined with remote sensing image coastline extraction. J. Surv. Mapp. Bull. 10, 89–92 (2020).
Wang, E. L., Hu, S. B., Han, H. W. & Liu, C. Q. Study on flow density of the Kai River in Heilongjiang River based on UAV low altitude remote sensing and OTSU algorithm. J. Water Resour. 53(1), 68–77 (2022).
Chen, J. J., Liu, R., Yang, X., Yang, M. & Yang, Y. T. Improved Otsu combined with morphology for water body information extraction. J. Remote Sens. Inf. 37(1), 101–109 (2022).
doi: 10.3390/rs15010101
Yu, X. K., Wang, Z. W., Wang, Y. H. & Zhang, C. L. Edge detection of agricultural products based on morphologically improved canny algorithm. J. Math. Probl. Eng. 6, 66 (2021).