A novel real-time arcing detection model of pantograph-catenary for high-speed train

The arcing happened in Pantograph-catenary system could damage railway operation. Arcing are characterized by short and volatile durations, making it challenging to rapidly detect and segment arcing of varying shapes and sizes. To address these issues, a fast deep learning based semantic segmentation model named ECA-ArcSC is proposed for real-time arcing detecting and segmentation task. Firstly, four downsampling convolution layers are utilized to capture spatial information of the arcing. Concurrently, a four-stage downsampling strategy is employed to calculate contextual information of the arcing. Finally, the spatial and contextual information of the arcing is aggregated to predict the final dense semantic segmentation output. Experimental results on the Pantograph-catenary arcing dataset and comparative analysis demonstrate the effectiveness of the proposed approach, achieving IoU of 76.08% and Recall of 84.3%. For inputs of size 512×512, ECA-ArcSC achieves FPS of 123 on an NVIDIA 4070Ti graphics card.