中山大学学报（自然科学版）（中英文）2025，Vol.64Issue(3)：119-128,10.DOI:10.13471/j.cnki.acta.snus.ZR20240336
基于NGDR和Logistic模型的高速公路图像雾浓度检测算法
Detection algorithm for highway image fog concentration based on NGDR and Logistic model
摘要
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(β̂,γ̂)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.关键词
高速公路/图像/雾浓度检测/NGDR/Logistic模型/回归分析/查找表Key words
highway/image/fog concentration detection/NGDR/Logistic model/regression analysis/lookup table分类
信息技术与安全科学引用本文复制引用
温立民,杨睿,聂磊,吴锋..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(3):119-128,10.基金项目
陕西省交通厅重点项目(20-38T) （20-38T）
西安市未央区科技计划(202121) （202121）
