Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (10): 3066-3074.DOI: 10.11772/j.issn.1001-9081.2020030337

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Rail surface defect detection method based on background differential with defect proportion limitation

CAO Yiqin, LIU Longbiao   

  1. School of Software, East China Jiaotong University, Nanchang Jiangxi 330013, China
  • Received:2020-03-23 Revised:2020-04-30 Online:2020-10-10 Published:2020-05-19
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61663009), the Key Program of the Science and Technology Supporting Plan of Jiangxi Province (20161BBE50081).


曹义亲, 刘龙标   

  1. 华东交通大学 软件学院, 南昌 330013
  • 通讯作者: 刘龙标
  • 作者简介:曹义亲(1964-),男,江西都昌人,教授,硕士,CCF会员,主要研究方向:图像处理、模式识别;刘龙标(1995-),男,江西赣州人,硕士研究生,主要研究方向:图像处理。
  • 基金资助:

Abstract: Aiming at the characteristics of rail surface images such as uneven illumination, limited discernible features, low contrast and changeable reflection characteristics, a background differential rail surface defect detection method based on defect proportion limitation was proposed. The method mainly includes five steps:pre-processing of rail surface images, background modeling and difference, defect proportion limitation filtering, maximum entropy threshold segmentation of defect proportion limitation and connected area labeling. Firstly, the column grayscale mean and median of the rail surface image were combined to perform the rapid background modeling, and the difference operation was carried out to the pre-processed image and the background image. Secondly, the feature with low defect proportion in the rail surface image was used to truncate the upper threshold limit of the defect proportion in order to enhance the contrast of the difference image. Thirdly, the maximum entropy threshold segmentation was improved by using this feature, the global variable weighting of the target entropy was carried out by using the adaptive weighting factor, and an appropriate threshold was selected to maximize the entropy value, so as to reduce the interference of noises such as shadow and rust while retaining the real defects. Finally, the connected area labeling method was used to perform the statistics of the defect areas in the segmented binary image, and the area with defect area lower than the rail damage standard was determined as the noise and removed, so as to realize the rail surface defect detection. Simulation results show that the new method can detect rail surface defects well, and its results have the recall rate, precision rate and weighted harmonic mean of 94.19%, 88.34% and 92.96% respectively, and the average mis-classification error of 0.006 4, so that the method has certain practical value.

Key words: rail surface image, background difference, defect proportion limitation, threshold segmentation, defect detection

摘要: 针对钢轨表面图像具有的光照不均匀、可识别特征有限、对比度低、反射特性易变等特性,提出基于缺陷比例限制的背景差分钢轨表面缺陷检测方法。该方法主要包括轨面图像预处理、背景建模与差分、缺陷比例限制滤波、缺陷比例限制最大熵阈值分割和连通区域标记5个步骤。首先结合轨面图像列灰度均值和列灰度中值进行快速背景建模,将预处理后的图像与背景图像进行差分操作;其次利用轨面图像缺陷占比较低的特征对差分图进行缺陷比例上限的阈值截断,以增强差分图的对比度;随后利用此特征改进最大熵阈值分割,采用自适应加权因子对目标熵进行全局可变加权,并选择出一个合适的阈值使熵值最大化,使得在保留真实缺陷的同时减弱诸如阴影、锈迹等噪声的干扰;最后利用连通区域标记法对阈值分割后的二值图像中的缺陷区域进行统计,并把缺陷面积低于钢轨损伤标准的区域判定为噪声并进行去除,以实现钢轨表面缺陷检测。仿真实验结果表明,新方法可以对钢轨表面缺陷进行很好的检测,其检测结果的召回率、精确率和加权调和平均值分别达到94.19%、88.34%和92.96%,平均错误分类误差值为0.006 4,具有一定的实用价值。

关键词: 钢轨表面图像, 背景差分, 缺陷比例限制, 阈值分割, 缺陷检测

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