基于轻量级YOLOv7算法的云南小米拉的快速检测
Fenghua Wang1, Jin Jiang1, Yu Chen1
1Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.
Frontiers in plant science
|June 21, 2023
概括
这项研究介绍了YOLOv7-PD,这是一个实时检测小米拉胡果的增强模型. 它提高了准确性,并降低了机器人收获系统的计算成本.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 实时水果检测对于自动收获系统至关重要.
- 现有的模型在密集或封闭的果实检测和高计算需求方面扎.
研究的目的:
- 开发一个高效和准确的果实检测模型Xiaomila胡.
- 为了降低计算成本并提高机器人收获的检测精度.
主要方法:
- 利用YOLOv7-tiny作为转移学习的基本模型.
- 将可变形卷积和SE注意力机制集成到YOLOv7微型架构中,创建了YOLOv7-PD.
- 在各种照明条件下收集了各种各样的小米拉水果图像.
主要成果:
- YOLOv7-PD实现了平均平均精度 (mAP) 的90.3%,超过了YOLOv7-tiny,YOLOv5s和Mobilenetv3.3的表现.
- 模型尺寸从12.7 MB减少到12.1 MB.
- 计算成本从13.1 GFlops降低到10.3 GFlops.
结论:
- 与现有模型相比,YOLOv7-PD在检测小米拉胡方面表现出卓越的性能.
- 拟议的模型为农业应用中实时果实检测提供了更有效的计算解决方案.
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