甘干节点识别算法基于改进的YOLOv5
Zhongjian Xie1,2, Yuanhang Li1,2, Yao Xiao1,2
1College of Electronic Information, Guangxi Minzu University, Nanning, China.
PloS one
|December 11, 2023
概括
这项研究介绍了G-YOLOv5s-SS,这是一种轻量级的人工智能模型,用于准确识别甘茎节点,提高种子预切割机的效率. 新型架构提高了检测精度,同时显著减少了模型大小和计算负载.
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:
- 对于种子预切割机的甘干节点识别通常需要高性能设备,导致效率低下.
- 现有的方法在实际农业应用中缺乏准确性和计算效率之间的最佳平衡.
研究的目的:
- 开发一种新的,轻量级的深度学习架构,以高效准确地检测甘干节点.
- 优化YOLOv5框架,提高农业机械的性能.
主要方法:
- 提出了一个新的轻量级架构,G-YOLOv5s-SS,基于YOLOv5.
- 修改了脊椎和部结构,以利用浅层特征并减少复杂性.
- 集成的Ghost轻量级模块和SimAM注意力机制,以提高特征提取和准确性.
主要成果:
- 在甘干节点识别方面,G-YOLOv5s-SS的平均精度为97.6%,超过YOLOv5基线的0.6%.
- 模型大小减少到2.6MB,具有1,129,340个参数和7.2G FLOP,这代表了大小和复杂性的显著减少.
- 与各种主流单阶段物体检测算法相比,在准确性和模型大小方面都表现出了卓越的性能.
结论:
- G-YOLOv5s-SS架构为甘干节点检测提供了一个高度准确和计算高效的解决方案.
- 开发的模型符合将其集成到甘种子预切割机的性能要求.
- 这种轻量级的方法平衡了高识别精度和降低模型复杂度,使其适用于资源有限的环境.
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