基于朴素贝叶斯分类的路面积雪状态检测  

Detection of the road snow coverage status based on naive Bayesian classifier

作　　者：孙中华[1] 蒋斌[1] 贾克斌[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《吉林大学学报（工学版）》2013年第S1期380-383,共4页Journal of Jilin University:Engineering and Technology Edition

基　　金：北京市教委科研计划资助项目(00200054K1006)

摘　　要：为解决路面积雪状态(轻微、严重)检测问题以保证行车安全,利用监控视频得到路面实时状态,采用朴素贝叶斯分类方法进行积雪状态检测。首先利用机器视觉和视频目标分割方法提取视频中路面视觉特征,然后采用朴素贝叶斯分类方法进行路面积雪状态分类,通过实验,综合比较了朴素贝叶斯分类与KNN分类、人工神经网络(ANN)、支撑向量机(SVM)在路面积雪状态检测问题中的有效性,结果表明,朴素贝叶斯分类器更适合积雪状态的分类。In order to detect the snow coverage status such as mild or heavy coverage on road to keep car running safe,snow coverage detection method together with naive Bayesian classification algorithm was proposed.Firstly,road visual feature was extracted from the video by the method of machine vision and video object segmentation.Then the road snow coverage status was classified with naive Bayesian classifier.The performance of KNN,ANN and SVM classifier for road snow coverage problem were compared.

关 键 词：路面积雪覆盖 特征信息 朴素贝叶斯分类 视频目标分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]