基于视觉注意力模型的高速铁路轨道病害检测  

High-speed Railway Track Damage Detection Based on the Model of Visual Attention

作　　者：夏利民[1] 刘涛[1] 肖永良[1,2] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410075 [2]湖南财政经济学院信息管理系,湖南长沙410205

出　　处：《信息与控制》2015年第3期353-358,共6页Information and Control

基　　金：国家自然科学基金资助项目(50808025);湖南省科技计划资助项目(S2013G2013);中南大学博士后基金资助项目(2014)

摘　　要：提出了基于视觉注意力模型的高速铁路轨道病害检测方法.采用基于稀疏采样和核密度估计的视觉注意力模型,得到高速铁路车载视频的显著图,进而提取视频中包含病害的区域;为了解决病害图像分类中的小样本问题,结合已标记样本和未标记样本,提出了基于生成模型的半监督分类方法,并用于高速铁路轨道病害识别分类.对3种典型的轨道病害进行了检查与识别实验,实验结果表明,该方法具有很高的检测率和识别率.Based on the model of visual attention, a track damage detection method for high-speed railways is proposed. The visual attention model based on sparse sampling and kernel density estimation is adopted to extract the damaged regions from the salient maps of a high-speed railway video. A semi-supervised classification based on a generative model is proposed to solve the problem of having a small sample in the damaged image classification. Then, it is used in the damaged images＇ recognition and classification, which makes use of both labeled and unlabeled samples. Three types of typical track damages are examined in the experiment and the results show that the proposed method has a high detection and recognition rate.

关 键 词：高速铁路轨道 视觉注意力模型 显著图 生成模型 半监督分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]