A Novel Arcing Detection Model of Pantograph–Catenary for High-Speed Train in Complex Scenes

Abstract

The Pantograph-catenary system is crucial for transferring electrical power from catenary lines to electrified train and the occurrence of arcing could damage railway operations, thus, it is important to detect arcing. Detecting arcing in complex scenes and detecting different size and shape of arcing is still a challenge. To overcome these issues, a robust image-based semantic segmentation model named ArcMSF is proposed for arcing detection of pantograph-catenary, which designs a novel hybrid multi-scale feature fusion model that aggregates Transformer with CNN to realize arcing pixel segmentation. A down-top decoder for combining low-level features with high-level features is designed to achieve multi-scale level arcing feature detecting in complex scenes. Inspired by the arcing image properties that arcing is always the brightest, the global max features and global threshold features are designed to augment the arcing features. Experiments on dataset IVAIS-PCA2021 and comparative experiments are conducted to demonstrate the effectiveness of the ArcMSF, which can achieve 89.13% segmentation accuracy and the fast inference speed of 23.84ms. Moreover, the detection results have a clear depiction of edge details.