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基于各向异性测度的路面三维图像裂缝识别

彭博 WANGKelvinC.P. 陈成 蒋阳升

唐智慧, 程杰, 方争楠. 基于FSDT的新老驾驶人危险感知差异性致因研究[J]. 西南交通大学学报, 2013, 26(3): 532-538. doi: 10.3969/j.issn.0258-2724.2013.03.022
引用本文: 彭博, WANGKelvinC.P., 陈成, 蒋阳升. 基于各向异性测度的路面三维图像裂缝识别[J]. 西南交通大学学报, 2014, 27(5): 888-895. doi: 10.3969/j.issn.0258-2724.2014.05.023
TANG Zhihui, CHENG Jie, FANG Zhengnan. Causal Mechanism of Difference between Experienced and Novice Drivers in Hazard Perception Based on Fuzzy Signal Detection Theory[J]. Journal of Southwest Jiaotong University, 2013, 26(3): 532-538. doi: 10.3969/j.issn.0258-2724.2013.03.022
Citation: PENG Bo, WANG Kelvin C.P., CHEN Cheng, JIANG Yangsheng. 3D Pavement Crack Image Detection Based on Anisotropy Measure[J]. Journal of Southwest Jiaotong University, 2014, 27(5): 888-895. doi: 10.3969/j.issn.0258-2724.2014.05.023

基于各向异性测度的路面三维图像裂缝识别

doi: 10.3969/j.issn.0258-2724.2014.05.023
基金项目: 

国家自然科学基金资助项目(51108391,61170041)

中央高校基本科研业务费专项资金科技创新项目(A0920502051208-99)

3D Pavement Crack Image Detection Based on Anisotropy Measure

  • 摘要: 为准确而完整地识别路面裂缝,提出了基于1 mm/像素的路面三维图像裂缝自动识别算法.该算法主要包括各向异性测度计算与自适应优化阈值分割、深度验证和多分辨率去噪处理3个部分.首先,针对路面图像像素特征,基于0°、45°、90°和135°四个方向的线性邻域的均值和标准差计算每个像素的各向异性测度(表征方向性的强弱),并应用最大类间方差法确定最优阈值,将路面图像分为强方向性和弱方向性像素两类;其次,根据半径为d的正方形邻域深度均值设定阈值,用方向性强且深度低于或等于该阈值的像素形成初步的裂缝图像;最后,将裂缝图像划分为多个子块,设计去噪模板对裂缝图像进行滑动窗口去噪处理,获得最终裂缝图像.基于166幅含有各类裂缝的三维路面图像(2 048×2 048像素)进行测试分析,结果显示,本文算法获得了较高的准确率(均值91.57%)和召回率(均值81.29%),最终以84.26%的F1 均值优于种子识别算法(F1 均值69.19%)、Canny边缘检测(F1 均值8.15%)和OTSU分割(F1 均值5.11%).

     

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出版历程
  • 收稿日期:  2013-03-22
  • 刊出日期:  2014-10-25

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