一个轻量级和高精度的激情果YOLO检测模型用于嵌入式设备的部署
Qiyan Sun1, Pengbo Li2, Chentao He2
1College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350100, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
一个新的轻量级模型,G-YOLO-NK,在复杂的果园条件下增强了激情果的检测. 这种模型实现了高精度和速度,使其非常适合嵌入式设备和果园机器人.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 在复杂的环境中准确和高效地检测激情果对于农业自动化至关重要.
- 现有的模型经常面临诸如背光,遮蔽和不同天气条件等挑战,限制了它们在现实世界中的应用.
研究的目的:
- 开发一种轻量级,高精度的对象检测模型,用于在具有挑战性的果园环境中检测激情果.
- 为了提高实时农业任务的嵌入式设备的检测速度和平均精度.
主要方法:
- 用轻量级的 GhostNet 模型取代了 YOLOv5 骨干,以减少参数和计算负载.
- 引入了一个新的功能分支,并重建了子网络的功能融合层,以增强功能集成.
- 采用知识蒸,将知识从教师模型转移到G-YOLO-NK学生模型,提高准确性.
主要成果:
- 该G-YOLO-NK模型实现了96.00%的平均精度,超过了原始YOLOv5s的1.00%.
- 模型大小减少了一半,为7.14 MB,在Jetson Nano上实时检测率为11.25 FPS.
- 与最先进的模型相比,在平均精度和检测能力方面表现出卓越的性能.
结论:
- 在复杂的果园环境中,G-YOLO-NK模型为实时激情果检测提供了有效的解决方案.
- 为开发智能果园采摘机器人和提高整体果园智能提供了宝贵的技术支持.
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