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基于改进YOLOv9s的受电弓弓角检测技术

机械制造与自动化 戴兵刚 杨琦 陈江龙 张俱珲

中图分类号:U279.3 文献标志码:A 文章编号:1671-5276(2025)03-0272-04

Pantograph Horn Detection Technology Based on Improved YOLOv9s

DAI Binggang1,YANG Qi1,CHEN Jianglong², ZHANG Juhui² (1. CRRC Guangzhou Rail Transit Equipment Co.,Ltd.,Guangzhou 511495, China; (2.NanjingUniversity of Science and Technology,Nanjing 21OO94,China) Abstract:Aimedat thepoorreal-time performanceand dificult equipmentdeplymentof horn detection incomplex scenes,the traditionalConvbeingreplacedbythelightweightconvolutional GhostConvbasedonYOLOv9s,theORPEA-RepN4 moduleis designedto improvethe feature extraction module,andtheCBAM moduleisadded toenhance detectionperformance.Thetest results show that compared with YOLOv9s,the proposed modelreduces thenumberof parameters of pantograph horn detection by (204号 32.8% ,and increases the detection accuracy of horn damage by 2.3 percent,which improves horn detection performance,and meets the requirements of edge device deployment and detection tasks.

Keywords:object detection;YOLOv9s;lightweight;attention mechanism

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