胡-YOLO:一种轻量级的模型,用于在复杂的环境中检测绿胡和定位采集点
Yikun Huang1,2,3, Yulin Zhong1, Deci Zhong1,3
1School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou, China.
Frontiers in plant science
|January 15, 2025
概括
这项研究介绍了Pepper-YOLO,这是一款用于绿收获机器人的轻量级AI模型. 它在复杂的环境中提高了检测和定位的准确性,同时减少了模型大小,以实现低成本部署.
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:
- 绿的收获受到水果背景颜色相似性和遮的阻碍.
- 高精度检测模型对于低成本的农业设备来说往往过于复杂.
研究的目的:
- 开发一种轻量级,高效的AI模型,用于同时检测绿和定位采集点.
- 为了实现在资源有限的农业硬件上部署.
主要方法:
- 根据YOLOv8n-Pose开发了一个改进的轻量级Pepper-YOLO模型.
- 整合了一个可逆的双金字塔结构,具有交叉层连接,以增强特征提取.
- 使用RepNCSPELAN4进行特征融合和C2fCIB模块进行优化大规模特征检测.
主要成果:
- 在复杂场景中实现了82.2%的物体检测精度和88.1%的收获点定位精度.
- 模型参数减少了38.3%,复杂性减少了28.9%,最终模型大小为4.3MB.
- 与最先进的方法相比,证明了更高的参数效率.
结论:
- 胡-YOLO提供高精度和实时性能,用于在具有挑战性的条件下收获绿.
- 该模型的轻量级设计使其适合在低成本农业机器人上部署.
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