SFEMM:一种基于YOLOv7x的棉双筒相匹配方法
Zhang Guohui1, Gulbahar Tohti1, Chen Ping1
1School of Mechanical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
这项研究引入了一种基于深度学习的双眼相匹配方法,用于棉花采摘机器人. 这种新方法在复杂的户外环境中显著提高了目标物体定位的准确性和速度.
科学领域:
- 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
- 农业技术 农业技术
背景情况:
- 精确的棉花空间定位对于在具有挑战性的户外环境中操作的自动采摘机器人至关重要.
- 传统的双筒照相匹配方法难以应对强光和多种场状的复杂性.
研究的目的:
- 为棉花采摘机器人开发一个准确和快速的双筒相匹配方法.
- 为了提高机器人在复杂的现实场景中定位目标棉花球的能力.
主要方法:
- 使用深度学习模型来确定棉花目标的精确位置和形状.
- 开发了一种新的匹配方程,用于精确的对象相关性.
- 集成的深度学习与定制匹配算法用于立体视觉.
主要成果:
- 与现有的算法相比,实现了显著更高的匹配精度:54.11% (BM),45.37% (SGBM),6.15% (PSMNet) 和12.21% (GC-net).
- 在比较方法中表现出最快的处理速度.
- 通过使用拟议的方法在太空中成功地定位了棉花目标.
结论:
- 拟议的双眼相匹配方法对于摘棉机器人来说是有效的和可行的.
- 这一进步通过增强的视觉感知为机器人收获能力提供了实质性的改进.
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