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YOLOv7-分支:农业机器人的朱叶分支检测模型
Ruijun Jing1, Jijiang Xu2, Jingkai Liu1
1School of Software, Shanxi Agricultural University, Taiyuan 030800, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
通过新的物体检测网络,提高了朱叶枝的智能收获. 这项技术通过准确识别和定位树枝来增强朱叶茶的生产,克服了诸如阻塞和噪音等现实世界的挑战.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 智能收获朱叶枝是提高朱叶茶生产的关键.
- 精确检测和定位朱叶枝对于智能收获系统至关重要.
- 现实世界的挑战,如背景噪音,遮蔽和形态变异,妨碍了准确的检测.
研究的目的:
- 开发一个先进的物体检测网络,用于精确识别和定位朱叶枝.
- 为了提高在复杂的果园环境中检测朱叶枝的准确性和稳定性.
- 为朱果园的智能管理和产量估计提供技术支持.
主要方法:
- 开发了一个基于YOLOv7的新型朱叶枝物体检测网络.
- 集成了极化自我注意和聚集激发模块,以抑制无关的背景噪音,并纳入空间信息.
- 使用具有隐性知识的高效解头和焦点-EIoU损失函数来提高特征提取和界限框定位精度.
主要成果:
- 拟议的模型实现了85%的精度,比YOLOv7-tiny.5%的改进.
- 该模型显示平均平均精度 (mAP@0.5) 为83.7%.
- 与现有模型相比,开发的方法表现出更高的识别率,回忆率和平均平均精度.
结论:
- 基于YOLOv7的增强网络有效地解决了用于智能收获的朱叶枝的检测挑战.
- 专用模块和损失功能的集成显著提高了检测准确度和定位精度.
- 这项技术为自动化朱果园管理和准确的产量估计提供了宝贵的支持.
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