现代电子技术2024，Vol.47Issue(23)：154-158,5.DOI:10.16652/j.issn.1004-373x.2024.23.023
基于支持向量机回归的道路能见度检测
Road visibility detection based on support vector machine regression
周俊 1买买提江·吐尔逊2
作者信息
- 1. 新疆大学 智能制造现代产业学院,新疆 乌鲁木齐 830017
- 2. 新疆交通基础设施绿色建养与智慧交通管控重点实验室,新疆 乌鲁木齐 830017||新疆大学 交通运输工程学院,新疆 乌鲁木齐 830017
- 折叠
摘要
Abstract
Road visibility is one of the important factors which affects traffic safety.In this paper,a vehicle-mounted video road visibility detection method based on support vector machine regression is proposed according to the characteristics of road images with low visibility in foggy days.Initially,the feature extraction region is determined by employing a region growth algorithm.Subsequently,the key features including atmospheric transmittance and image entropy are extracted.These features are then utilized to train a support vector regression(SVR)model.Finally,the trained model is employed to detect the visibility of the road image.The experimental results show that the overall accuracy of the proposed method in visibility detection within 400 m can reach 90.1%,so the proposed method can meet the needs of practical application in the transportation industry.关键词
能见度检测/支持向量回归/暗通道先验/区域增长法/大气透射率/图像熵Key words
visibility detection/SVR/dark channel prior/region growing/atmospheric transmittance/image entropy分类
信息技术与安全科学引用本文复制引用
周俊,买买提江·吐尔逊..基于支持向量机回归的道路能见度检测[J].现代电子技术,2024,47(23):154-158,5.
