基于曲率滤波和改进GMM的钢轨缺陷自动视觉检测方法  

Automatic visual detection method of railway surface defects based on curvature filtering and improved GMM

作　　者：张辉[1,2,3] 金侠挺 Wu Q.M.Jonathan 贺振东 王耀南[2] Zhang Hui;Jin Xiating;Wu Q. M. Jonathan;He Zhendong;Wang Yaonan(College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;University of Windsor, Windsor N9B3P4, Canada)

机构地区：[1]长沙理工大学电气与信息工程学院,长沙410114 [2]湖南大学电气与信息工程学院,长沙410082 [3]温莎大学,温莎N983P4

出　　处：《仪器仪表学报》2018年第4期181-194,共14页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61401046);国家科技支撑计划(2015BAF11B01);湖南省教育厅科学研究项目(17C0046)资助

摘　　要：针对传统钢轨检测技术的效率低下、精度不足、安全隐患等问题,提出了基于曲率滤波和改进高斯混合模型（GMM）的钢轨表面缺陷检测方法。首先,提出了基于垂直投影的区域定位算法和灰度对比算法,克服现场工况复杂、轨面反射不均、信道噪声干扰的难题;考虑到图像信号受强工况噪声干扰,研究了具有隐式计算和曲面保持特性的曲率滤波法进行图像去噪;建立了基于马尔科夫随机场（MRF）的高斯混合模型完成表面缺陷的精确快速分割。最后,设计了＂区域定位-灰度均衡化-滤波-分割＂的实验流程,实验结果验证了算法的有效性,检测性能达到了精确度92.0%,相比其他方法更加精确、快速,具有更好的鲁棒性。This paper proposes a visual detection method for rail surface defect based on curvature filtering and improved Gaussian mixture model（ GMM） aiming atthe problems of low efficiency,lack of precision and safety hazard for traditional rail inspection technique. First of all,this paper proposes a ROI location algorithm based on vertical projection and gray contrast algorithm,which overcome the difficulties of.complex field condition and rail surface reflectance inequality and signal channel noise interference,Considering the situation that the image signal is interfered by strong noise,a curvature filteringmethod with implicit computing and surface preserving power is studied to conductthe image denoising and keep the image details. Next,an improved Gaussian mixture model based on Markov random field（ MRF） is established to achievethe accurate and rapid surface defect segmentation. In the end,the experiment process of region location-gray equalization-filtersegmentation was designed. The experiment results verify the effectiveness of theproposed method,the detection accuracy of 92. 0% is reached,and the proposed method is more accurate,faster and more robust than other methods.

关 键 词：钢轨表面缺陷 视觉检测 曲率滤波 马尔科夫随机场 改进高斯混合模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TH89[自动化与计算机技术—计算机科学与技术]