在嵌入式系统上使用YOLO部署无人机和地面机器人的技术方面,用于智能物流.
Wissem Dilmi1,2, Sami El Ferik1,2, Fethi Ouerdane1,2
1Department of Control and Instrumentation Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
这项研究比较了YOLOv5和YOLOv8在无人机等嵌入式系统上的物流自动化. 它引入了一个GUI,用于更好的可视化和部署指南,以提高运营效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 物流自动化利用图像处理和人工智能提高效率和弹性.
- 在资源有限的嵌入式平台上部署深度学习来实现实时物流是一个挑战.
研究的目的:
- 为了比较YOLOv5和YOLOv8在物流中嵌入式平台上的对象检测性能.
- 为实时检测结果可视化和过开发一个用户友好的GUI.
- 为无人机和地面机器人基础物流提供部署指南.
主要方法:
- 使用COCO和自定义物流数据集对YOLOv5和YOLOv8进行比较分析.
- 推断速度,准确性和数据集特定指标的评估.
- 开发一个用于对象可视化的图形用户界面 (GUI).
- 在Python和C#环境中研究部署策略.
主要成果:
- 嵌入式系统上的YOLOv5和YOLOv8的性能指标 (速度,准确性).
- 展示GUI在实时对象可视化和过方面的有效性.
- 对Python和C#部署对性能和可扩展性的影响进行比较分析.
结论:
- YOLOv8显示了嵌入式平台上增强物流自动化的潜力.
- 开发的GUI显著提高了对象检测系统的实际可用性.
- 优化部署策略对于机器人物流中的高效资源利用至关重要.
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