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动态时间规整下的列车车钩缓冲图像区域校正

赵耀,陈建胜(中国农业大学, 北京 100083;中国科学院遥感与数字地球研究所, 北京 100094)

摘 要
目的 在铁路货车故障轨边图像检测系统(TFDS)采集的车钩缓冲区域影像中,托架、车钩等关键部件是刚体,但部件之间的连接是软连接,存在相对位移,造成传统的全局校正模型在该类图像校正中无法实现高精度校正,本文基于动态时间归整(DTW)方法,提出一种基于DTW区域划分的影像校正方法,实现影像的高精度校正。方法 本文将成像良好的车钩缓冲图像作为标准图像,首先对待校正图像进行预处理,消除标准图像与待校正图像之间在灰度、角度与尺度方面的差异,并针对车钩缓冲图像在车辆行进的垂直方向上偏移较小的特点,将2维图像校正问题转化为1维匹配问题,与待校正的车钩缓冲图像进行基于DTW的区域匹配,实现关键部件所在区域的区域划分,在对应的区域内分别进行校正,能够达到较高的校正精度。结果 将传统的车钩缓冲图像校正方法与本文方法进行校正精度对比,经验证,本文方法的均方误差比传统校正方法小20个像元,并且本文方法成功实现了关键部件的区域划分,为后面的关键部件识别奠定了基础。结论 经验证,本文校正方法适用于定向移动的复合刚体部件的区域校正,能够实现车钩缓冲图像中各个软连接部件的高精度校正,满足车钩缓冲图像校正的需要。
关键词
Region correction for train coupler buffer images based on dynamic time warping

Zhao Yao,Chen Jiansheng(China Agricultural University, Beijing 100083, China;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

Abstract
Objective Trouble of moving freight car detection system (TFDS) has become an important system for train daily safety check system. Given the diversity and complexity of train faults, manual train fault detection remains the main method, which requires considerable manpower and includes unstable safety factors. Therefore, automatic train fault detection has become an urgent need of the TFDS system. Several soft-connected components exist in coupler buffer images, which lead to the difficulty of automatic correction between two images. Method We use dynamic time warping (DTW) to realize region division among several soft-connected components in the same image. Based on comparative analysis between the same regions in standard and origin images, we can determine the difference of each component and obtain the train fault detection result. Before region division, we preprocess the coupler buffer image to eliminate the image rotation and scale problem. We maintain the longitudinal image gray value to form a one-dimensional vector. In the one-dimensional space, we conduct vector matching based on the DTW method and realize region division in coupler buffer. We calculate the similarity of soft components between two images in different column inspection stations. Result During preprocessing, we can reduce the gray difference of the same parts in two images by histogram matching. With the coarse correction model, we can eliminate the rotation and size differences in the same parts in two images, which are ready for vertical gray value statistical calculation. In DTW matching, the vertical gray statistical value can reflect the distribution of soft connection parts. The matching method is suitable to separate different parts and achieve part matching. Conclusion Given that several parts of a coupler buffer are softly connected, accurate matching of all parts is difficult to realize by the global correction model. In this study, we adopt the DTW region matching method based on the vertical gray, which can effectively separate different components. Therefore, we can correct each region and facilitate subsequent comparative analyses.
Keywords
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