简化YOLOv7以在嵌入式设备上快速准确地检测油菜品种
Siqi Gu1,2, Wei Meng1,2, Guodong Sun1,2
1School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究介绍了一种轻量级的YOLOv7模型,用于嵌入式设备上的实时菜种子检测. 优化的模型显著减少参数和推断时间,同时保持高精度准确农业的高精度.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 实时种子检测对农业至关重要,但现有的方法难以准确或部署嵌入式设备.
- 传统的方法往往缺乏对资源有限的农业硬件所需的效率.
研究的目的:
- 开发一个高效的实时种子品种检测模型,适合嵌入式设备.
- 优化YOLOv7对象检测模型用于农业应用,特别是菜种子检测.
主要方法:
- 在YOLOv7模型上应用了一种二维修剪技术 (空间和通道).
- 实验验证实了空间修剪的有效性.
- 为获得最佳性能,选择了自定义比率层次的通道修剪.
主要成果:
- 修剪后的YOLOv7型号实现了96.89%的mAP,比96.68%有所增加.
- 模型参数从36.5M减少到9.19M.
- 在Raspberry Pi 4B上推断时间从4.48秒减少到1.18秒.
结论:
- 拟议的削减YOLOv7模型非常适合在嵌入式设备上部署.
- 该模型可以在各种农业环境中准确有效地实时检测菜种子.
- 这一进步通过改进的设备分析来支持精准农业.
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