Detection algorithm for highway image fog concentration based on NGDR and Logistic model

WEN Limin; YANG Rui; NIE Lei; WU Feng

doi:10.13471/j.cnki.acta.snus.ZR20240336

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Detection algorithm for highway image fog concentration based on NGDR and Logistic model

Research articles | 更新时间：2025-11-14

- Detection algorithm for highway image fog concentration based on NGDR and Logistic model
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 64, Issue 3, Pages: 119-128(2025)
- 作者机构：
  
  1.长安大学电子与控制工程学院，陕西西安 710064
  2.汉滨区农村公路养护中心，陕西安康 725000
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.ZR20240336
  CLC： TP391.9
- Received：26 November 2024，
  
  Accepted：19 December 2024，
  
  Published Online：24 January 2025，
  
  Published：25 May 2025
- 稿件说明：
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温立民,杨睿,聂磊等.基于NGDR和Logistic模型的高速公路图像雾浓度检测算法[J].中山大学学报(自然科学版)(中英文),2025,64(03):119-128.

WEN Limin,YANG Rui,NIE Lei,et al.Detection algorithm for highway image fog concentration based on NGDR and Logistic model[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(03):119-128.
温立民,杨睿,聂磊等.基于NGDR和Logistic模型的高速公路图像雾浓度检测算法[J].中山大学学报(自然科学版)(中英文),2025,64(03):119-128. DOI： 10.13471/j.cnki.acta.snus.ZR20240336.

WEN Limin,YANG Rui,NIE Lei,et al.Detection algorithm for highway image fog concentration based on NGDR and Logistic model[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(03):119-128. DOI： 10.13471/j.cnki.acta.snus.ZR20240336.

摘要

提出了基于Logistic函数拟合S型散点图的雾浓度评定算法。首先，提取LIVE标准图集归一化灰度差-比散点图先验；基于散点曲线与视场雾浓度的一一对应关系，引入Logistic函数并推导出适合回归分析的模型。其次，采用迭代搜索法确定纵向高斯分布的最佳回代样本点，以提高检测精度。最后，建立参数估计（

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）的查找表，采用计算相关系数和遍历搜索查找的方法实现雾浓度等级评定。同场景不同浓度图像样本1的测试表明，真实图像的PM2.5与查找表PM2.5的相关系数达0.99，检测误差小于2.9%；近似场景不同浓度高速公路图像样本2的测试表明，真实图像PM2.5与查找表PM2.5值的相关系数达0.98，检测误差小于1.8；执行

效率对比测试表明，本文算法对于300 kB样本图像的处理时间为19.8 s，低于同精度数据驱动的深度视觉算法；检测精度对比测试表明，本文算法优于其它典型算法。

Abstract

A fog concentration evaluation algorithm based on logistic function fitting S-type scatter plot was proposed. Firstly， a scatter-plot prior of normalized gray difference-ratio（NGDR） from the standard LIVE image set was extracted，and Logistic functions was introduced to derive a regression analysis model based on the one-to-one correspondence between the scatter curve and the fog concentration. Secondly， the iterative search method was used to determine the optimal sample points of longitudinal Gaussian distribution to improve the detection accuracy. Finally， a lookup table for parameter estimation（

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7.95866632

）was established， and both calculating the correlation coefficient and traversal search were used to evaluate concentration grade. The test of image samples with different concentrations in the same scene 1 shows that the correlation coefficient between PM2.5 in the real image and PM2.5 in the lookup table was 0.99， and the detection error was less than 2.9%. The test results of highway image sample 2 with different concentrations in the approximate scene show that the correlation coefficient is 0.98， and the detection error is less than 1.8. The comparative test of execution efficiency shows that the processing time of the proposed

algorithm for 300 kB sample images is 19.8 s， which is lower than that of the data-driven depth vision algorithm with the same precision. The comparative test of detection accuracy shows that the proposed algorithm is better than other typical algorithms.

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references

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