Key technology of image enhancement and edge extraction for heavy rail

MI Zeng-zhen; XIE Zhi-jiang; CHEN Tao; CHU Hong-yu; FAN Bing

doi:10.3788/OPE.20122007.1645

您当前的位置：

首页 >

文章列表页 >

Key technology of image enhancement and edge extraction for heavy rail

更新时间：2020-08-12

- Key technology of image enhancement and edge extraction for heavy rail
- Optics and Precision Engineering Vol. 20, Issue 7, Pages: 1645-1652(2012)
- 作者机构：
  
  重庆大学机械传动国家重点实验室,重庆 400044
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122007.1645
  CLC： TP391.4
- Received：01 March 2012，
  
  Revised：12 April 2012，
  
  Published Online：10 July 2012，
  
  Published：10 July 2012
- 稿件说明：
移动端阅览
米曾真, 谢志江, 陈涛, 楚红雨, 范兵. 重轨图像增强与边缘提取的关键技术[J]. 光学精密工程, 2012,20(7): 1645-1652

MI Zeng-zhen, XIE Zhi-jiang, CHEN Tao, CHU Hong-yu, FAN Bing. Key technology of image enhancement and edge extraction for heavy rail[J]. Editorial Office of Optics and Precision Engineering, 2012,20(7): 1645-1652
米曾真, 谢志江, 陈涛, 楚红雨, 范兵. 重轨图像增强与边缘提取的关键技术[J]. 光学精密工程, 2012,20(7): 1645-1652 DOI： 10.3788/OPE.20122007.1645.

MI Zeng-zhen, XIE Zhi-jiang, CHEN Tao, CHU Hong-yu, FAN Bing. Key technology of image enhancement and edge extraction for heavy rail[J]. Editorial Office of Optics and Precision Engineering, 2012,20(7): 1645-1652 DOI： 10.3788/OPE.20122007.1645.

摘要

针对重轨图像两个边缘像素特征不一致

传统边缘算子检测法难以精确提取边缘的问题

提出了一种新的边缘提取方法。该方法利用灰度强对比度拉伸算法对重轨表面和背景进行差异化拉伸

增强边缘信息

削弱背景信息。运用最大方差比算法选取增强后图像的最佳阈值实现二值化。最后

运用递归连通域标识法定位边缘像素坐标

完成图像分割。对随机选取的30幅图像进行分析表明:处理后的图像边缘灰度特征明显增强

有效地抑制了表面纹理及虚假边缘。重轨表面像素宽度波动减少到-0.64%~0.34%。离散预处理算法通过遍历寄存器全局数组

减少分割时间至10.165 s。该方法在抗干扰性、准确性及时效性等方面优于传统边缘算子检测法

适用于在线工业检测系统。

Abstract

As the two edge pixels of a heavy rail image is not identical

the classical edge operators are difficult to achieve the edge extraction and segmentation. Therefore

this paper proposed a new algorithm to enhance and extract images. A strong contrast stretching algorithm was used to stretch the rail surface and the background differently

enhance the edge information and weaken the background information. Then

the maximum variance method was taken to select the optimal threshold to implement the binarilization. Finally

the recursion connected domain marker algorithm was used to locate the pixel coordinates of edge to achieve the image segmentation. 30 images were chosen to a discretion experiment

and results indicate that the gray features of image edge are enhanced clearly

surface textures and false edges are restrained availably. Moreover

the pixel width fluctuating range is reduced from -0.64% to 0.34%. With the discrete pretreatment algorithm via addressing global array of a register

the segmentation time has been decreased to 10.165 s. The algorithm is better than the classical edge operators in the precision

correctness and the timeliness and is more suitable for on-line detection systems.

关键词

Keywords

references

魏天斌. 高速铁路发展趋势及武钢重轨生产策略 [J]. 钢铁研究,2005,33(6):52-55. WEI T B. Development trend of high-speed railroad and strategies of wiscos heavy rail production[J]. Research on Iron & Steel, 2005, 33(6):52-55. (in Chinese)

孟佳. 钢轨表面缺陷识别系统的设计与研究 . 成都:西南交通大学 ,2005. MENG J. Design and research of recognition system on railway surface defects .Chengdu:Southwest Jiaotong University, 2005. (in Chinese)

苏兰海,潘爱文,马祥华. 热轧窄带钢模糊边界的精确求解 [J]. 北京科技大学学报,2008,30(3): 307-310. SU L H, PAN A W, MA X H. Exact solution to fuzzy edge of hot rolled narrow strips[J]. Journal of Beijing University of Science and Technology, 2008, 30(3):307-310 . (in Chinese)

张闯. 铁轨缺陷检测系统的算法优化 . 大连:大连海事大学 ,2005. ZHANG CH. Optimization of railroad track flaw inspection system . Dalian:Dalian Maritime University, 2005. (in Chinese)

NOBUYOSHI M, NICOLAS W. Burgers vector determination in deformed perovskite and post-perovskite of CaIrO3using thickness fringes in weak beam dark-field images[J]. Ultramicroscopy,2009, 109(6):683-692.

SUN T H, TSENG C C, CHEN M S. Electric contacts inspection using machine vision [J]. Image and Vision Computing, 2010, 28(6):890-910.

ZUMPANO G, MEO M. A new damage detection technique based on wave propagation for rails[J]. International Journal of Solids and Structures, 2006, 43(5):1023-1046.

王庆香,李迪, 张舞杰,等. 软性电路板金面缺陷的无监督检测 [J]. 光学精密工程,2010,18(4): 981-987. WANG Q X,LI D,ZHANG W J, et al.. Unsupervised defect detection for gold surface of flexible printed board [J]. Opt. Precision Eng., 2010,18(4):981-987 . (in Chinese)

CHANG J H, FAN K C, LANG Y. Multi-modal gray-level histogram modeling and decomposition[J].Image and Vision Computing,2002,20(3):203-216.

陈涛. 热态重轨表面缺陷在线检测系统关键技术研究 . 重庆:重庆大学 ,2011. CHEN T. The key technology study on the surface defects detection of hot heavy rail .Chongqing:Chongqing University,2011.(in Chinese)

GONZALEZ R C, WOODS R E, EDDINS S L. Digital Image Processing Using MATLAB[M]. Gatesmark Publishing, 2009.

OTSU N. A threshold selection method from gray-level histograms[J]. IEEE transactions on systems, man and cybernetics,1979,9(1):62-66 .

CHUNG K L, TSAI C L. IEEE Transactions on systems, Man and Cybernetics[J]. Applied Mathematics and Computation, 2009,212(2):396-408 .

KESHENG W, EKOW O, KENJI S. Optimizing two-pass connected-component labeling algorithms[J]. Pattern Analysis & Applications, 2009, 12(2):117-135 .

王金涛,刘文耀,路烁. 流域分割算法在细胞图像分割中的应用 [J]. 西南交通大学学报,2002,37(3):227-231. WANG J T, LIU W Y, LU SH. Application of watershed algorithm to cell image segmentation[J]. Journal of Southwest Jiaotong University, 2002, 37(3):227-231 . (in Chinese)

杨永敏,樊继壮,赵杰. 强反射表面缺陷图像预处理 [J]. 光学精密工程,2010,18(10):2288-2296. YANG Y M, FAN J ZH, ZHAO J. Preprocessing for highly reflective surface defect image[J]. Opt. Precision Eng., 2010, 18(10):2288-2296 . (in Chinese)

Views

519

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive color compensation and multi-scale fusion for underwater image enhancement

Computed tomography image segmentation of cell pole piece via strip attention mechanism

Degradation remote sensing image quality enhancement based on frequency-domain-spatial-domain hybrid attention

Underwater image enhancement using joint texture perception and color histogram features

Cross-level feature aggregation image enhancement with dual-path hybrid attention

Related Author

LIU Cuiyin

JING Tengyun

WANG Feng

CHEN Yuanshuai

LIU Zefang

LONG Chao

LIU Xueshuan

HAN Yan

Related Institution

School of Information Engineering and Automation， Kunming University of Science and Technology

School of Physics and Material Sciences， Guangzhou University

ICT Research Center， Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education， Chongqing University

College of Optoelectronic Engineering， Chongqing University

Project Management Center， Equipment Department of Rocket Force

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰