Contact Wire Support Defect Detection Using Deep Bayesian Segmentation Neural Networks and Prior Geometric Knowledge

The contact wire support (CWS) is an important catenary component that maintains the contact wire height and stagger. The direct impact of the pantographs makes the CWS a vulnerable part of the catenary. Recently, automatic catenary inspection using computer vision and pattern recognition has been introduced to improve railway operation safety. However, the automated detection of CWS defects remains to be further studied. This paper proposes a novel CWS defect detection system that consists of three stages. First, the Faster R-CNN network is adopted to localize the key catenary components, and the image areas that contain CWS components are obtained. Then, the CWS components are segmented using a Bayesian fully convolutional catenary components segmentation network (CCSN) that fuses different level features of the backbone network. The CCSN is not only able to perform accurate CWS components segmentation, but also capable of evaluating the model uncertainty by Monte Carlo dropout. Finally, the defect status is determined using the proposed criteria, which are defined according to the geometries of the components. Experiments on the Hefei-Fuzhou high-speed railway line indicate that this approach can be applied to the CWS defect detection.