计算机科学 ›› 2014, Vol. 41 ›› Issue (1): 130-133.

• 2013 CCF人工智能会议 • 上一篇    下一篇

一种基于空间滤波的钢轨表面擦伤检测改进算法

赵宏伟,黄雅平,王胜春,李清勇   

  1. 北京交通大学计算机与信息技术学院 北京100044;北京交通大学计算机与信息技术学院 北京100044;北京交通大学计算机与信息技术学院 北京100044;北京交通大学计算机与信息技术学院 北京100044
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61272354,4,61105119),中央高校基本科研业务费(2012JBM039,2JBM027,2011JBZ005),北京交通大学轨道交通控制与安全国家重点实验室自主研究课题(RCS2012ZT007),北京邮电大学智能通信软件与多媒体北京市重点实验室开放课题资助

Rail Surface Defect Detection Algorithm Based on Spatial Filtering

ZHAO Hong-wei,HUANG Ya-ping,WANG Sheng-chun and LI Qing-yong   

  • Online:2018-11-14 Published:2018-11-14

摘要: 钢轨表面擦伤检测是保障铁路运输安全的重要手段之一。应用图像处理和模式识别技术来处理钢轨数字图像,检测并定位擦伤区域是一种可行且发展迅速的研究手段。课题组在前期工作中提出了一种鲁棒实时的钢轨表面擦伤检测算法,该算法首先对钢轨图像进行灰度对比度增强,在此基础上定位可疑擦伤区域并进行判定。算法对于常规擦伤图像具有较高的检测性能,但对于钢轨图像包含多处擦伤且擦伤区域灰度值差异较大的情况,往往造成漏检。针对原算法的不足,提出了一种基于空间滤波的钢轨表面擦伤检测改进算法,该算法对原算法检测到的擦伤区域进行钢轨灰度图均值填充,并对填充后的图像进行二次检测,在重新生成的灰度对比度图中,原检测图像中灰度值不明显的擦伤区域的灰度对比度值得到增强,从而增加了检出的可能性。经实验结果验证,改进算法具有较高的检测性能:在总的时间耗费没有明显增加的情况下,检测的平均准确率为90.8%,平均漏检率为4.0%,较原算法有较大改善。

关键词: 钢轨表面擦伤,检测,算法改进,空间滤波

Abstract: Detection of the rail surface defects is important to the safety of railway transportation.Leveraging the techniques of image processing and pattern recognition to detect and locate defects is a viable and rapidly developing research techniques.In previous work,a Rail Surface Defect Detection (RSDD) was proposed by our research group.RSDD first enhances the contrast of the rail image,on this basis locates and detects suspicious defects.It has a high detection performance for conventional rail image,but misses some defects in the cases of rail image that contains multiple defects and has high gray value difference among them.This paper put forward an Improved Rail Surface Defect Detection (I-RSDD),which fills the detected defect areas with mean gray value of original rail image and then detects the intermediate result image again.In the rebuilt contrast image,thereby,the contrast value of the defect areas which are not obvious in the original image is enhanced and the possibility for the defect areas to be detected is increased.Our experimental results demonstrate that I-RSDD has high detect property:in the case of no noticeable increase of total time consumption,the average rate of accuracy of detection is 90.8%,and the average detection error rate is 4.0%,which has substantially improvement compared with RSDD.

Key words: Rail surface defect,Detection,Algorithm improvement,Spatial filtering

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