Research on Safety Inspection Technology of EMU Pantographs in Rainy Days
Pantograph is a key component for the EMU to obtain power from the OCS,therefore its integrity and stability are crucial to the safe operation of the EMU.Using images collected by the on-board video monitoring system to inspect the pantograph safety is currently the mainstream inspection method.In the face of severe weather such as rain and snow,how to better process images to achieve accurate inspection is particularly important.The paper introduces deep learning for video deraining of the self-built dataset for rainy-day pantograph status,while data augmentation methods based on the Generative Adversarial Network(GAN)is used to enhance the dataset for different categories.Then based on the modification of the YOLOv5 algorithm and the introduction of the SCCONV structure,the feature extraction capability of the Backbone region is improved with a test model accuracy of 98％.The inspection practice helps realize real-time analysis on high-performance edge computing board,offering new idea for real-time intelligent video analysis of railway pantographs in China.
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