ArcSE: A Dual-Branch Semantic Segmentation Model for Robust Pantograph–Catenary Arcing Detection in Complex Environments

The pantograph-catenary system (PCS) is a key equipment for electric trains to obtain electrical energy from the traction power supply system. As an abnormal phenomenon in the PCS, pantograph-catenary arcing (PCA) directly affects the current collection quality and operational safety of electric trains. Therefore, it is very important to achieve precise detection of PCA. At present, aiming at the difficulties in detecting small arcing and the poor performance of arcing detection in complex environments in the visual detection task of PCA, a semantic segmentation model arcing segmentation (ArcSE) based on feature enhancement is proposed. This model designs a PCA segmentation model that includes semantic feature branches (SFBs), detail feature branches (DFBs), and feature enhancement mechanisms (FEMs). To address the difficulty of detecting small arcing, a dual-branch structure is designed, which utilizes the semantic information extracted from the SFB to adjust the detail feature map in the DFB, filters out interfering features, and retains small arcing features. Aiming at the difficulty of identifying arcing targets in complex scenes, an FEM is designed, merging the arcing features with the detailed features at different scales through a multiscale features fusion strategy. At the same time, based on the learnable visual center module, the difference between arcing features and background features is further strengthened, effectively improving the robustness of the model. Experiments were conducted on the constructed dataset to validate the effectiveness of ArcSE, with a segmentation accuracy of 89.70% and an inference speed of 14.91 ms.