在果园种植园中使用你只能看一次的实例细分来检测未切割的杂草
Rizky Mulya Sampurno1,2, Zifu Liu1, R M Rasika D Abeyrathna1,3
1Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
自主机器人除草机现在可以通过新的视觉系统精确地导航果园. 这种由人工智能驱动的系统准确地识别杂草和障碍物,使机器人在具有挑战性的果园环境中能够有效地除草.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在果园中机械除草是劳动密集型的,并带来风险.
- 对于自主系统来说,由于果园结构阻碍了GNSS信号,内部杂草的除草是具有挑战性的.
- 现有的自主杂草除草者在准确的杂草和障碍物识别方面扎.
研究的目的:
- 开发一个智能视觉模块用于自主机器人杂草除草器.
- 为了能够准确地识别果园行中的杂草和障碍物.
- 支持机器人操作在以前无法进入的内部区域.
主要方法:
- 使用YOLO实例细分算法 (YOLOv5n-seg,YOLOv5s-seg,YOLOv8n-seg,YOLOv8s-seg) 在来自梨园的自定义数据集上进行训练.
- 收集并预处理了5000张图像用于培训和测试.
- 基于检测准确度,复杂性和推断速度的评估模型,用于边缘设备上的实时应用.
主要成果:
- 较小的YOLO模型 (基于YOLOv5和YOLOv8) 显示出更高的效率.
- 选择YOLOv8n-seg是因为它在资源有限的设备上具有卓越的细分精度和可接受的性能.
- 开发的视觉系统实现了有效的对象识别,用于机器人内部杂草除草.
结论:
- 基于深度学习的视觉系统可以显著增强自主机器人在果园中的杂草除草.
- 由于其精度和效率的平衡,YOLOv8n-seg是机器人杂草机中的视觉模块的合适选择.
- 拟议的系统解决了自主果园管理的关键挑战,为更高效,更精确的杂草控制铺平了道路.
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