Research purposes:To address the issues of low efficiency,low accuracy,and high investment cost in high-definition image acquisition and detection of the overall suspension string of high-speed rail overhead contact system,a high-definition acquisition method for the overall suspension string based on front-end image recognition triggering and deep learning algorithm fusion localization is proposed.Research conclusions:(1)Using an embedded FPGA+ARM dual processing platform,the YOLOv5 based suspension string positioning algorithm is embedded in the hardware structure.Image processing hardware acceleration and recognition filtering are achieved through FPGA,and high-precision recognition of image data is achieved through ARM.The overall suspension string image detection rate is 99%,with a detection time of 4 ms per image,meeting the requirements of real-time detection.(2)By combining FPGA and ARM,a dedicated image processing algorithm chip is built-in to filter out a large number of useless images and obtain clear overall suspension string images.The accuracy and recall rate trained and tested on the dataset are 100%,and the actual circuit is greater than 99%.(3)By integrating the embedded FPGA+ARM hardware system with YOLOv5's suspension string positioning algorithm,the system detection cost has been reduced and the technical application scope has been expanded.This has a reference value for promoting high-quality intelligent construction and intelligent operation and maintenance of overhead contact system.
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