动车组运行故障动态图像比对分析方法

Image Comparison and Analysis of Trouble of Moving EMU

动车组运行故障动态图像检测系统(TEDS),通过在轨边安装布置高速线阵采集相机,实现对运行中列车的全方位监控。利用获得的高质量图像,通过机器学习和模式识别,实现列车故障的自动化诊断和检测。但是线阵相机拍摄的图像易受动车速度的影响,在图像水平方向上存在几何变形,给后续目标的自动识别和检测带来了困难。为了解决这个问题,设定一组基准图像,对其他时间段所获得的目标图像分别按照对应的基准图像进行配准和重分割,尽量减小列车速度对成像变形的影响。结合TEDS,利用多分辨率下的图像快速配准方法,实现了后续目标图像的快速分割与对齐。提出了一种改进的图像差影技术,通过将对齐之后的目标图像与历史标准图像进行比对分析,快速实现动车故障区域的自动定位和检测。

The trouble of moving electric multiple-unite（EMU）detection system（TEDS）,can monitor comprehensively the status of China railway high-speed（CRH）train,by using the high-speed linear cameras located besides the railway.We fulfill the automatic identification and inspection of the target in the high-quality image by using the machine learning and pattern recognition technology.However,the image taken by the linear camera is susceptible to the speed of vehicle,and there is a geometric deformation in the horizontal direction of image,which brings difficulties to the automatic recognition and detection of the subsequent target.To solve this problem,we set up a set of reference images,register and re-divide the images obtained in other time,according to the corresponding reference image,so as to minimize the influence of train′s speed on imaging deformation.Combined with TEDS,we use multi-resolution image fast registration method to achieve the rapid segmentation and alignment of subsequent target images.Hence,we propose an improved image subtraction technique,in order to quickly and automatically locate and detect the fault region by comparing and analyzing the aligned image pairs.