A pantograph–catenary system (PCS) is an important part of a railway power supply system, which is an interface of the power supply system and the electric locomotive. The quality of a traction power supply depends on the stability of the contact between a pantograph and a catenary. Therefore, it is necessary to monitor the contact state by detecting the contact point (CPT) between the pantograph and the catenary. Recently, automatic CPT detection methods based on video monitoring have been introduced to improve the railway operation safety. However, the existing methods were still not stable enough in complex backgrounds. To improve the stability of CPT detection, we proposed a method combining a deep convolutional network with handcrafted features to detect the CPT. The proposed method consists of two stages. First, a deep pantograph network (DPN) was adopted to segment the pantograph strip. The DPN was mainly composed of a deep pantograph detection network (DPDN) and a deep pantograph segmentation network (DPSN). Then, the edge detection and the Hough transform were used to detect the contact line above the pantograph. Concretely, the CPT was obtained by finding the intersection of the contact line and the upper surface of the pantograph strip. The experimental results demonstrated the robustness and the accuracy of the proposed method.

中文翻译：

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一种基于深度卷积网络的鲁棒受电弓-悬链线相互作用状态监测方法

受电弓-接触网系统（PCS）是铁路供电系统的重要组成部分，是供电系统与电力机车的接口。牵引电源的质量取决于受电弓与接触网接触的稳定性。因此，需要通过检测受电弓与接触网之间的接触点（CPT）来监测接触状态。近年来，基于视频监控的自动CPT检测方法被引入以提高铁路运营安全。然而，现有方法在复杂背景下仍然不够稳定。为了提高 CPT 检测的稳定性，我们提出了一种将深度卷积网络与手工特征相结合的方法来检测 CPT。所提出的方法由两个阶段组成。第一的，采用深度受电弓网络（DPN）来分割受电弓带。DPN主要由深度受电弓检测网络（DPDN）和深度受电弓分割网络（DPSN）组成。然后，利用边缘检测和霍夫变换检测受电弓上方的接触线。具体而言，CPT是通过找到接触线与受电弓带上表面的交点来获得的。实验结果证明了所提出方法的鲁棒性和准确性。CPT是通过找到接触线和受电弓带上表面的交点来获得的。实验结果证明了所提出方法的鲁棒性和准确性。CPT是通过找到接触线和受电弓带上表面的交点来获得的。实验结果证明了所提出方法的鲁棒性和准确性。