Pantograph–catenary arcing detection via morphology-aware and efficient feature modeling

Abstract During the operation of high-speed trains, the pantograph-catenary system is responsible for 
continuously delivering traction power from the catenary to the locomotive. The occurrence of 
pantograph-catenary arcing not only indicates the deterioration of dynamic power transmission 
quality but also severely ablates contact components and causes electromagnetic interference, 
thereby affecting train operation safety. Therefore, detecting pantograph-catenary arcing is of 
great significance. Considering the high uncertainty in the morphology and scale of arcing 
behavior in complex scenes, achieving reliable, fast, and real-time detection remains a 
challenge. To this end, we propose a real-time detection method for pantograph-catenary arcing 
based on morphology-aware and efficient feature modeling (Morphology-Aware Efficient 
Feature Modeling RT-DETR, MEFM-RTDETR). This method introduces a dynamic adaptive 
convolution kernel weight generation mechanism to model arcing features of varying scales, 
orientations, and morphologies, thereby enhancing the network's morphology-aware capability. 
Second, a multi-scale feature enhancement and fusion network (MEFusion) is constructed to 
fully integrate shallow detail information with deep semantic information, addressing the issue 
of the loss of tiny arcing features. Finally, an efficient additive attention mechanism is 
introduced into the intra-scale feature interaction module (AIFI) to enhance the modeling 
capability of global contextual information. Experimental results show that MEFM-RTDETR 
achieves a 2.63% improvement in mAP50, a 2.04% improvement in mAP50-95, a 4.9 GFLOPs 
reduction in computational complexity, and approximately a 6 M reduction in the number of 
parameters compared to the baseline model on the arcing detection task.