基于计算机视觉的钢轨焊缝自动定位方法

doi:10.3969/j.issn.0255-8297.2019.06.009

应用科学学报 ›› 2019, Vol. 37 ›› Issue (6): 844-850.doi: 10.3969/j.issn.0255-8297.2019.06.009

基于计算机视觉的钢轨焊缝自动定位方法

刘松, 熊邦书

南昌航空大学图像处理与模式识别江西省重点实验室, 南昌 330063

收稿日期:2018-10-21 修回日期:2019-03-09 出版日期:2019-11-30 发布日期:2019-12-06
通信作者: 熊邦书,教授,研究方向:模式识别、智能信号处理、图像处理,E-mail:xiongbs@126.com E-mail:xiongbs@126.com
基金资助:
国家自然科学基金（No.61866027）；江西省重点研发计划基金（No.20171BBE50013）；南昌航空大学研究生创新专项基金（No.YC2017019）资助

Automatic Orientation Method for Rail Weld Based on Computer Vision

LIU Song, XIONG Bangshu

Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province, Nanchang Hangkong University, Nanchang 330063, China

Received:2018-10-21 Revised:2019-03-09 Online:2019-11-30 Published:2019-12-06

摘要/Abstract

摘要： 为了实时检测钢轨焊缝边缘错边量合格情况，提出了基于计算机视觉的钢轨焊缝自动定位方法.首先，采用中值滤波对焊缝图像进行噪声去除；其次，采用限制对比度自适应直方图均衡算法和直方图均衡化法进行焊缝图像增强；然后，采用双阈值OTSU法进行图像分割，突出轨头和焊缝区域图像，并采用连通域法提取轨头和焊缝区域轮廓；最后，采用多次最小二乘直线拟合法获取焊缝拟合直线，计算拟合直线与轨头上边缘直线的交点作为定位点，实现钢轨焊缝自动定位.对60 kg/m钢轨焊缝区域的检测实验表明，所提方法具有精度高、稳定性好的优点，可用于焊轨基地焊缝的在线实时自动检测.

关键词: 计算机视觉, 图像增强, 焊缝边缘检测, 自动定位

Abstract: In order to detect the edge misalignment of rail weld in real time, an automatic positioning method for the rail weld based on computer vision is proposed. First, the method of median filtering is used to remove the noise in a weld image. Second, the weld image is enhanced by using the contrast limited adaptive histogram equalization (CLAHE) algorithm and the histogram equalization method. Third, the image is segmented by the double threshold OTSU method to highlight the rail head and the weld region, and the contours of the rail head and the weld region are extracted by using the method of connected-domain. Finally, the weld fitting line is obtained by multiple least squares fitting, and the intersection of the fitted straight line and the top edge line of the rail head are calculated as the positioning point, accordingly, the rail weld is automatic positioned. Through a test of the weld region of 60 kg/m rail, the method performs with high precision and stability, and can be used for measuring the rail weld automatically in real-time on line.

Key words: computer vision, image enhancement, weld edge detection, automatic positioning

中图分类号:

U213.9

刘松, 熊邦书. 基于计算机视觉的钢轨焊缝自动定位方法[J]. 应用科学学报, 2019, 37(6): 844-850.

LIU Song, XIONG Bangshu. Automatic Orientation Method for Rail Weld Based on Computer Vision[J]. Journal of Applied Sciences, 2019, 37(6): 844-850.

参考文献

[1] 张琪,李力,宋宏图,等. 无缝线路钢轨焊接技术发展现状及趋势[J]. 热加工工艺,2017, 46(3):10-12. Zhang Q, Li L, Song H T, et al. Development status and trends of seamless rail welding technologies[J]. Hot Working Technology, 2017, 46(3):10-12. (in Chinese)
[2] Yu Y, Machemehl R B, Xie C. Demand-responsive transit circulator service network design[J]. Transportation Research Part E, 2015, 76:160-175.
[3] Antipov A G, Markov A A. Evaluation of transverse cracks detection depth in MFL rail NDT[J]. Russian Journal of Nondestructive Testing, 2014, 50(8):481-490.
[4] 苏日亮. 面向钢轨轨底缺陷检测的电磁超声换能器研究[D]. 哈尔滨:哈尔滨工业大学,2015.
[5] 高炜欣,胡玉衡,武晓朦,等. 埋弧焊X射线焊缝缺陷图像分类算法研究[J]. 仪器仪表学报,2016, 37(3):518-524. Gao W X, Hu Y H, Wu X M, et al. Sub-arc X-ray welding defect image classifying algorithm[J]. Journal of Instrumentation, 2016, 37(3):518-524. (in Chinese)
[6] Sinisterra A J, Dhanak M R, Ellenrieder K V. Stereovision-based target tracking system for USV operations[J]. Ocean Engineering, 2017, 133:197-214.
[7] 闵永智,岳彪,马宏锋,等. 基于图像灰度梯度特征的钢轨表面缺陷检测[J]. 仪器仪表学报,2018,39(4):220-229. Min Y Z, Yue B, Ma H F, et al. Rail surface defects detection based on gray scale gradient characteristics of image[J]. Journal of Instrumentation, 2018, 39(4):220-229. (in Chinese)
[8] Dubey A K, Jaffery Z A. Maximally stable extremal region marking-based railway track surface defect sensing[J]. IEEE Sensors Journal, 2016, 16(24):9047-9052.
[9] 吴禄慎,张丛,万超,等. 基于机器视觉的钢轨表面检测光学模型的研究[J]. 铁道标准设计,2017, 61(5):53-57. Wu L S, Zhang C, Wan C, et al. Study on optical model for rail surface detection based on machine vision[J]. Railway Standard Design, 2017, 61(5):53-57. (in Chinese)
[10] 蒋超,牛宏侠. 基于改进Radon变换的直线钢轨识别算法[J]. 铁道标准设计,2017, 61(4):19-22. Jiang C, Niu H X. Linear rail recognition algorithm based on improved Radon transform[J]. Railway Standard Design, 2017, 61(4):19-22. (in Chinese)
[11] Feng H, Jiang Z, Xie F, et al. Automatic fastener classification and defect detection in visionbased railway inspection systems[J]. IEEE Transactions on Instrumentation & Measurement, 2014, 63(4):877-888.
[12] 王洋,余祖俊,朱力强,等. 基于CNN的高速铁路侵限异物特征快速提取算法[J]. 仪器仪表学报,2017, 38(5):1267-1275. Wang Y, Yu Z J, Zhu L Q, et al. Fast feature extraction algorithm for hige-speed railway clearance intruding objects based on CNN[J]. Journal of Instrumentation, 2017, 38(5):1267-1275. (in Chinese)
[13] Verma K, Singh B K, Thoke A S. An enhancement in adaptive median filter for edge preservation[J]. Procedia Computer Science, 2015, 48:29-36.
[14] 姚宜斌,黄书华,孔建,等. 空间直线拟合的整体最小二乘算法[J]. 武汉大学学报(信息科学版),2014, 39(5):571-574. Yao Y B, Huang S H, Kong J, et al. Total least squares algorithm for fitting spatial straight lines[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5):571-574. (in Chinese)

基于计算机视觉的钢轨焊缝自动定位方法

Automatic Orientation Method for Rail Weld Based on Computer Vision

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	蔡晨, 潘斌, 刘振宁. 用井字结构光对规则部件进行三维测量[J]. 应用科学学报, 2017, 35(1): 107-116.
[2]	付强1，李一兵1，汤春瑞2. 表面波和Retinex结合的水声图像增强方法[J]. 应用科学学报, 2012, 30(5): 505-510.
[3]	孙贤斌;李德华尹杰熊才权. 机器视觉系统中摄像机的神经网络定标[J]. 应用科学学报, 2007, 25(5): 486-486 .
[4]	黄凯奇, 吴镇扬, 王桥. 色彩恒常性在彩色图像增强中的应用[J]. 应用科学学报, 2004, 22(3): 322-326.