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Updated: Jul 5, 2025

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轻量级道障碍物检测基于改进的YOLOv5

Yingjie Li1, Chuanyi Ma2, Liping Li3

  • 1Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括

这项研究引入了改进的YOLOv5模型,用于建筑机器人的自主障碍物检测,提高了安全性. 新型号显著提高了检测速度和效率,同时保持了对危险环境的高精度.

关键词:
改进了YOLOv5的功能轻量级的模型轻量级的模型.对象检测检测对象检测对象检测

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