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计算机工程 ›› 2013, Vol. 39 ›› Issue (3): 25-30. doi: 10.3969/j.issn.1000-3428.2013.03.006

所属专题: 智能交通专题

• 轨道交通专题 • 上一篇    下一篇

铁轨表面缺陷的视觉检测与识别算法

唐湘娜,王耀南   

  1. (湖南大学电气与信息工程学院,长沙 410082)
  • 收稿日期:2012-05-02 出版日期:2013-03-15 发布日期:2013-03-13
  • 作者简介:唐湘娜(1987-),女,硕士研究生,主研方向:机器视觉,智能检测;王耀南,教授、博士生导师
  • 基金资助:

    国家自然科学基金资助重点项目(60835004);国家“863”计划基金资助项目(2007AA04Z244)

Visual Inspection and Classification Algorithm of Rail Surface Defect

TANG Xiang-na, WANG Yao-nan   

  1. (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)
  • Received:2012-05-02 Online:2013-03-15 Published:2013-03-13

摘要: 提出一种铁轨表面缺陷的视觉检测与识别算法。设计铁轨表面缺陷视觉检测与识别系统的总体结构,基于水平投影法提取铁轨表面区域,采用逻辑操作组合检测结果,使用BP神经网络进行缺陷分类。实验结果表明,该算法能准确地检测与识别铁轨表面的疤痕和波纹擦伤这2种缺陷,分类正确率分别达到99%和95%。

关键词: 铁轨, 表面缺陷检测, 水平投影法, 灰度补偿, 机器视觉, BP神经网络

Abstract: This paper proposes a visual inspection and classification algorithm of rail surface defect. An overall structure of the visual inspection and classification system for rail surface defects is designed. It extracts the rail surface subarea based on horizontal projection method, combines the inspection result based on logic or operation, and classifies the defect based on BP neural network. Experimental results show that the algorithm is able to detect and identify the two rail surface defect, scarring and corrugated abrasion accurately, the accuracy of classification is 99% and 95%.

Key words: rail, surface defect inspection, horizontal projection method, gray scale compensation, machine vision, BP neural network

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