关于改进的YOLOv8和深度摄像机的融合研究,用于子番茄茎采摘点的识别和定位
Guozhu Song1, Jian Wang1, Rongting Ma1
1College of Software, Shanxi Agricultural University, Taigu, China.
Frontiers in plant science
|December 16, 2024
概括
这项研究引入了一种增强的YOLOv8模型,用于精确地识别和定位串串番茄果茎. 改进的模型实现了91.3%的成功率,有助于机器人收获.
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:
- 准确的果茎识别对于高效的串番茄收获至关重要.
- 挑战包括视觉障碍,相似的颜色,不规则的生长,以及影响深度感应的短/细茎.
研究的目的:
- 开发一个增强的YOLOv8模型,与深度摄像头集成,用于精确识别和定位串串西红果茎采摘点.
- 提高机器人收获系统的准确性和效率.
主要方法:
- 修改后的YOLOv8 (FastMLCA-YOLOv8) 包含c2f瓶和MLCA注意力机制,用于果茎识别.
- 优化了K-means算法,使用K-means++和Silhouette系数进行果茎细分.
- 使用腐蚀和张精细化算法进行脱和骨线提取.
- 使用RGB-D摄像头数据获得3D坐标信息的二次提取方法.
主要成果:
- 在复杂条件下的采集点,FastMLCA-YOLOv8模型实现了91.3%的识别成功率.
- 与标准YOLOv8模型相比,精度提高了2.8%.
- 采集点的深度值误差为±2.5毫米.
结论:
- 拟议的算法准确地识别和定位串串番茄采摘点,解决复杂环境中的挑战.
- 这项技术支持开发自主串串番茄采摘机器人.
- 这项研究为推进串串番茄收获技术提供了重要的支持.
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