基于机器视觉与YOLOv5的吊弦高清图像采集方法

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Abstract 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.

Key words： overhead contact system(OCS) integral suspension string machine vision deep learning YOLOv5

Received: 27 March 2024

PACS:

U226.5+1

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	Articles by authors
	XU Jianguo
	HAN Jianmin
	LIU Yan
	WANG Jianchao

Cite this article:

XU Jianguo,HAN Jianmin,LIU Yan, et al. A High Identification Image Acquisition Method for Suspension String Based on Machine Vision and YOLOv5[J]. Journal of Railway Engineering Society, 2024, 41(9): 81-86.

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https://tdgcxb.crec.cn/EN/ OR https://tdgcxb.crec.cn/EN/Y2024/V41/I9/81

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