Railway Automatic Switch Stationary Contacts Wear Detection Under Few-Shot Occasions

Railway Automatic Switch (RAS) plays a crucial role in Turnout Switching System (TSS). The size of RASs Stationary Contacts (SCs) directly affects connectivity of the pivotal control and feedback circuit, which further influences the remaining useful life of TSS. However, it is impossible to avoid normal wear and tear or fractures of SC during daily operation, resulting in size change of SCs. Therefore, it is vital to monitor the size of SCs. However, due to lack of wear samples, it is hard to design automatic algorithms for this task, especially for developing currently popular deep learning. To this end, this paper proposes a computer vision method for railway automatic switch stationary contacts wear detection under few-shot occasions. Our method includes two key modules: a Few Shot SC DETection (FSDet) module and a Contour-based Size MEAsurement (CSMea) module, which together form a system that achieves accurate SC detection and size monitoring. The FSDet module formulates a multi-template deep feature matching pipeline, which plays the role of detecting all SCs in an image under the few shot manner. Then, the CSMea module takes the above detected SC patches as input and detects wear regions utilizing contour features and key point features. Finally, size of SCs can be calculated in image level by computing average pixels distance in wear regions and rescaled into real world level using image calibration tools. Experimental results demonstrate that the proposed method can accurately and robustly detect and measure the size of different SC structures in few-shot occasions.

Automated summary

This paper proposes a computer vision method for detecting wear on railway automatic switch stationary contacts in few-shot scenarios. The method includes two key modules, which can accurately detect and measure the size of different stationary contacts.