基于深度学习的高铁接触网定位器检测与识别

Detection and recognition of high-speed railway catenary locator based on Deep Learning

高铁接触网安全监测的主要方法是采用可见光高清相机捕捉接触网零部件的图像序列,通过图像处理和计算机视觉技术实现对零部件的检测、识别与跟踪.在整个监测系统中,定位器检测识别是必要的基础工作.传统的目标检测算法受限于特征描述子的设计,难以依靠人工设计出具有通用性、鲁棒性、高精度的特征描述子.于是提出基于Faster R-CNN模型实现高精度的接触网定位器检测,同时采用Hough变换检测出定位器的骨架轮廓,并通过滤线机制筛选出定位器的最优拟合直线段,为定位器坡度的非接触式精准测量做好基础性工作.

High-speed rail monitoring is conducted mainly by adopting image processing and computer vision technology to detect, identify and track catenary components in image sequences taken by the visible light high-definition camera. In the entire monitoring system, the detection and recognition of the locator constitutes the very basis. It is difficult to design the feature descriptor with the characteristics of versatility, robustness and high-accuracy by using traditional target detection algorithms. The detection of the high-accuracy locators based on the Faster R-CNN framework has been realized. Meanwhile, the Hough transform is used to detect the skeleton outline of the locator, and the optimal fitting straight line of the locator is extracted by the filtering mechanism, which paves the way for the non-contact precision measurement of the slope of the locators.