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A Combined Multi-Mode Visibility Detection Algorithm Based on Convolutional Neural Network

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Abstract

The accuracy of visibility detection greatly affects daily life and traffic safety. Existing visibility detection methods based on deep learning rely on massive haze images to train neural networks to obtain detection models, which are prone to overfit in dealing with small samples cases. In order to overcome this limitation, a large amount of measured data are used to train and optimize the convolutional neural network, and an improved DiracNet method is proposed to improve the accuracy of the algorithm. On this foundation, combined multi-mode algorithm is proposed to achieve small samples fitting and train an effective model in a short time. In this paper, the proposed improved DiracNet and the combined multi-mode algorithm are verified by using the measured atmospheric fine particle concentration data (pm1.0, pm2.5, pm10) and haze video data. The validation results demonstrate the effectiveness of the proposed algorithm.

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Acknowledgements

This work is supported by the opening fund for State Key Laboratory of Severe Weather of China Meteorological Administration (Grant No.2021LASW-A07), the Universities Natural Science Research project of Jiangsu Province (Grant No.19KJB510048), the Opening fund for National Key Laboratory of Solid Microstructure Physics in Nanjing University (Grant No.M30006), the Post-doctoral fund of Jiangsu Province (Grant No.1701132B).

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Authors and Affiliations

  1. Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing, Jiangsu, China

    Mu Xiyu, Xu Qi, Zhang Qiang, Wang Hongbin & Zhou Linyi

  2. Nanjing Joint Institute for Atmospheric Sciences, Nanjing, Jiangsu, China

    Mu Xiyu, Wang Hongbin & Zhou Linyi

  3. Jiangsu Air Traffic Management Branch Bureau of CAAC, Nanjing, Jiangsu, China

    Xu Qi

  4. Meteorology Center of North China Air Traffic Management Bureau, CAAC, Beijing, China

    Zhang Qiang

  5. Jiangsu Telecommunication Limited Company, Pukou Branch, Jiangsu, Nanjing, China

    Ren Junch

  6. Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China

    Ren Junch

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  1. Mu Xiyu
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  2. Xu Qi
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  3. Zhang Qiang
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  4. Ren Junch
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  5. Wang Hongbin
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  6. Zhou Linyi
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Correspondence to Mu Xiyu.

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Xiyu, M., Qi, X., Qiang, Z. et al. A Combined Multi-Mode Visibility Detection Algorithm Based on Convolutional Neural Network. J Sign Process Syst 95, 49–56 (2023). https://doi.org/10.1007/s11265-022-01792-1

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  • DOI: https://doi.org/10.1007/s11265-022-01792-1

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