一种基于特征的对象识别和定位方法用于狼.
Renwei Wang1, Dingzhong Tan1, Xuerui Ju1
1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究介绍了一种新的图像细分算法,用于wolfberry收获机器人. 它提高了在具有挑战性的照明条件下识别狼果实和树枝的准确性.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 对象识别和定位对于自动收获机器人至关重要.
- 在农业环境中,非结构化的照明和堵塞构成了重大挑战.
研究的目的:
- 为狼收获机器人开发一个先进的图像细分算法.
- 为了提高在复杂条件下对狼果实和树枝进行细分的准确性.
主要方法:
- 一个功能融合算法,将Lab (a-channel) 和YIQ (I-channel) 颜色空间与波形变换相结合,用于果实细分.
- 实验室色彩空间中的K-means集群算法,加上形态处理和长度过,用于分支细分.
- 对分支的抓地点坐标的定位.
主要成果:
- 在复杂的照明和遮蔽条件下,在500个样本中获得了78%的狼果的细分精度.
- 在精确的分支细分和抓地点定位方面表现出高准确性.
- 该算法有效地处理照明变化和遮蔽.
结论:
- 与传统方法相比,拟议的算法显著提高了细分和本地化准确性.
- 为现场 wolfberry 收获机器人的视觉系统提供必要的技术支持.
- 为自动化农业收获操作提供实用参考.
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