On-line Detection of Pantograph Offset Based on Deep Learning

A safe train operation relies on the well-contact of pantograph and the power grid above, therefore identifying the state of pantograph plays an vital role. Among all of the malfunctions, pantograph offset is a strong reflection of the state. We have proposed a new approach to reconstructing the three-dimensional (3D) information of the bow by substituting the offset with connection of left and right horns. To locate the region of pantograph horn, we refer to an efficient deep learning method, named Single Shot MultiBox Detector (SSD). In the located area, region growing or Wiener filtering is applied to extract connected components and enhance the prospects. For processed images, grayscale morphological gradients is adopted to obtain image edges, on which Harris corner detection can provide dozens of potential corner-points. These points containing one correct horn point needs selection by assuming the lowest one is the best, only when the background noise is as low as possible. After attaining the two-dimensional (2D) image coordinates of the horns, binocular stereo vision method makes contribution to reconstructing 3D coordinates. Using the 3D coordinate line of the left and right horns to represent the pantograph offset can easily reflect the degree of deviation by comparing it with the initial location of pantograph. Our approach is of great significance to prevent malfunction, which are about to arise later, such as horn loss or deflection, spark of pantograph and catenary system contact-point. The detection of the pantograph offset provides a strong guarantee for maintaining railway traffic safety.