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高铁扣件的自适应视觉检测算法

范宏 侯云 李柏林 熊鹰

范宏, 侯云, 李柏林, 熊鹰. 高铁扣件的自适应视觉检测算法[J]. 西南交通大学学报, 2020, 55(4): 896-902. doi: 10.3969/j.issn.0258-2724.20180496
引用本文: 范宏, 侯云, 李柏林, 熊鹰. 高铁扣件的自适应视觉检测算法[J]. 西南交通大学学报, 2020, 55(4): 896-902. doi: 10.3969/j.issn.0258-2724.20180496
FAN Hong, HOU Yun, LI Bailin, XIONG Ying. Adaptive Detection Algorithm for High-Speed Railway Fasteners by Vision[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 896-902. doi: 10.3969/j.issn.0258-2724.20180496
Citation: FAN Hong, HOU Yun, LI Bailin, XIONG Ying. Adaptive Detection Algorithm for High-Speed Railway Fasteners by Vision[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 896-902. doi: 10.3969/j.issn.0258-2724.20180496

高铁扣件的自适应视觉检测算法

doi: 10.3969/j.issn.0258-2724.20180496
基金项目: 四川省科技计划项目(2018GZ0361)
详细信息
    作者简介:

    范宏(1985—),男,博士研究生,研究方向为机器视觉与模式识别,E-mail:hg.fan@foxmail.com

    通讯作者:

    李柏林(1962—),男,教授,研究方向为计算机图形图像处理,E-mail:blli62@263.net

  • 中图分类号: TP391.41

Adaptive Detection Algorithm for High-Speed Railway Fasteners by Vision

  • 摘要: 为了实现高铁缺陷扣件的准确、快速和自动化检测,提出一种基于图像处理技术的高铁扣件自适应视觉检测算法. 针对高铁扣件图像的特性,使用改进的LBP (local binary pattern)算子提取扣件的显著特征;在扣件特征图的基础上,采用模板匹配算法得到扣件区域在原始图中的精确位置,进而得到扣件子图并用扣件的位置信息校验定位结果;以相邻两个扣件子图的差值作为判断依据,如果差值大于预设的阈值,相应的扣件则被判断为缺陷扣件. 将该检测算法应用于高铁工务部门提供的真实扣件图. 研究结果表明:本文提出的自适应扣件检测算法在雨天的表现最差,检出率为96%,误检率为0.50%;在晴天的表现最好,检出率为100%,误检率为0.22%;在不同天气、光照、环境下的综合检出率为99%,综合误检率为0.33%.

     

  • 图 1  原始LBP的邻域结构及其权重

    Figure 1.  Neighborhood structures of original LBP and its weights (N = 8,R = 1)

    图 2  改进LBP的邻域结构及其权重

    Figure 2.  Neighborhood structures of improved LBP and its weights (N = 4,R = 1)

    图 3  使用不同邻域半径的扣件改进LBP

    Figure 3.  Fastener image coded by improved LBP with different neighborhood radius

    图 4  原始LBP与改进LBP对扣件噪声图像的编码结果对比

    Figure 4.  Comparison of coding results for fastener noise images using original LBP and improved LBP

    图 5  模板与掩膜

    Figure 5.  Template and mask

    图 6  改进LBP编码的原始图像

    Figure 6.  Original image encoded by improved LBP

    图 7  扣件定位

    Figure 7.  fastener localization

    图 8  相邻扣件的位置关系

    Figure 8.  Positional relationship of adjacent fasteners

    图 9  相邻扣件及其特征图对比

    Figure 9.  Comparison of adjacent fasteners and their feature maps

    图 10  原始测试图像

    Figure 10.  Original test images

    表  1  扣件定位结果

    Table  1.   Results of fastener localization

    定位方法 输入图像数/(× 104 幅)图像大小/像素包含扣件数/(× 104 个)准确定位/幅定位准确率/%
    Yang[10]31 000 × 3501827 50491.68
    Feng[14]31 000 × 3501828 27294.24
    Original LBP31 000 × 3501828 95696.52
    本文方法31 000 × 3501829 98899.96
    下载: 导出CSV

    表  2  扣件识别结果(Td = 0.3)

    Table  2.   Results of fastener recognition algorithm (Td = 0.3)

    图像类别输入图像总数/(× 103 幅)正常图像数/幅缺陷图像数/幅正确检出/幅错误检出/幅检出率/%误检率/%
    高光照 5 4 968 32 32 16 100.00 0.32
    低光照 5 4 964 36 35 21 97.22 0.42
    雨天 5 4 975 25 24 25 96.00 0.50
    晴天 5 4 966 34 34 11 100.00 0.22
    新线路 5 4 972 28 28 12 100.00 0.24
    老线路 5 4 955 45 45 14 100.00 0.28
    综合 30 29 800 200 198 99 99.00 0.33
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-16
  • 修回日期:  2018-11-07
  • 网络出版日期:  2018-11-13
  • 刊出日期:  2020-08-01

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