Aiming at the requirements of intelligent equipment construction in the intelligent high-speed railway system V2.0,a high-speed railway line environment video detection system is developed.The system uses high real-time and dual-mode compensation technology for dynamic imaging,which overcomes motion blur under high-speed operation conditions and ambient light interference in open scenes.An intelligent recognition algorithm for foreign object intrusion is presented based on the faster region-based convolution-al neural networks(Faster-RCNN)framework and YOLO v8,achieving the online detection of abnormal train operating environment status for the high-speed train platform.The application software of video dynamic detection of line environments is developed,which realizes the dynamic real-time monitoring and abnormal data management of abnormal train operation environments.Experimental re-sults show that the system can meet the high-definition imaging and foreign object intrusion detection of the line environment with a velocity of up to 450 km/h,and the accuracy of defect detection is greater than or equal to 90％.
维普
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