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基于YOLOv5-RACF模型的果收获机器人
Fengwu Zhu1, Weijian Zhang1, Suyu Wang1
1College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China.
Biomimetics (Basel, Switzerland)
|August 28, 2024
概括
本研究介绍了一种YOLOv5-RACF算法,用于自动采摘果,实现高检测精度和高效的水果处理. 该系统可靠地在真正的果园条件下采摘果.
科学领域:
- 农业工程 农业工程
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 自动收获对于解决劳动力短缺和提高果生产效率至关重要.
- 精确的果识别和直径计算对于机器人抓取和收获至关重要.
研究的目的:
- 开发和评估YOLOv5-RACF算法,用于自动检测果和直径测量.
- 将这个算法与一个机器人系统集成,用于在果园环境中自主收获果.
主要方法:
- 使用YOLOv5-RACF算法进行果识别和直径计算.
- 采用机器人操作系统 (ROS) 控制机器人机动,激光雷达绘图,导航和机器人手臂操作.
- 在真实的果园环境中进行测试,以评估系统性能.
主要成果:
- 取得了98.748% (mAP@0.5) 和90.02% (mAP@0.5:0.95) 的平均果检测准确度.
- 在0.13秒内计算出果直径,精确度为1-3毫米.
- 在平均9秒内完成一个果采摘周期 (采摘和返回).
结论:
- 拟议的YOLOv5-RACF算法和集成的机器人系统证明了自动果收获的高效率和可靠性.
- 该系统在真正的果园中的性能验证了其在实际农业应用中的潜力.
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