Locator slope calculation via deep representations based on monocular vision

The locator is the key component to control the track of contact wire in overhead catenary system (OCS) for high-speed railway. Once the locator slope is out of bound, it would pose a huge hazard to the safety of the high-speed trains and threat to the human life and property damage. In this work, a novel end-to-end locator slope calculation framework is presented for locator slope real-time inspection in high-speed railway system. The pipeline is composed of two stages: locator contour detection and slope calculation. In order to precisely detect the locator contours in OCS images captured from high-speed extreme environments, a novel detection mechanism including rough detection and fine fitting is proposed. For the fast slope calculation through only one camera, monocular vision model is modified by two novel assumptions to calculate the locator space coordinates. Rigorous experiments are performed across a number of standard locator slope calculation benchmarks, showing a large improvement in the precision and speed over all previous methods. Finally, the effectiveness of proposed framework is demonstrated through a real-world application of the high-speed rail OCS inspection system.