摘要
铁轨表面缺陷严重影响铁路系统的运行质量和安全,提出了基于图像传感器的铁轨表面缺陷视觉检测算法,并重点研究了图像增强和自动阈值分割。采用局部对比度测量法增强铁轨图像对比度,使缺陷区域明显突出于背景区域;采用改进的最大类间方差法分割铁轨增强图像,消除了更多的噪声且保持了必要的缺陷信息。实验结果表明:铁轨表面缺陷检测的准确率和检全率分别达到86.1%和91.9%。
Rail surface defects impact the riding quality and safety of railway system seriously. Therefore,visual detection algorithm for rail surface defects based on image sensor is proposed,and it focuses on image enhancement and automatic threshold segmentation. Local contrast measurement method is adopted to enhance the contrast of rail images and highlights defects from background notably. Improved maximum between-cluster variance（ Otsu） is adopted to segment rail enhancement images,and it eliminates more noise and keeps the necessary information of defects. Experimental results show that accuracy and recall ratio is 86. 1 % and 91. 9 %,respectively.
出处
《传感器与微系统》
CSCD
2015年第9期141-144,共4页
Transducer and Microsystem Technologies
基金
江苏省自然科学基金资助项目(BK20131107)
关键词
铁轨表面缺陷检测
图像传感器
图像增强
自动阈值分割
rail surface defects detection
image sensor
image enhancement
automatic threshold segmentation
