TBF-YOLOv8n:基于YOLOv8n改进的轻量级茶叶芽检测模型
1School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430048, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
一个新的轻量级深度学习模型,Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n),提高了茶叶芽检测的准确性和效率. 这种模型显著降低了计算复杂性,为先进的智能茶叶收获铺平了道路.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 茶叶质量,采摘效率和智能收获对于茶叶行业的经济效益至关重要.
- 对于茶叶芽检测的深度学习模型往往受到高计算复杂性的困扰,限制了实际应用.
研究的目的:
- 开发一种轻量级和高效的深度学习模型,用于茶叶本地化检测.
- 为了提高茶叶芽检测的准确性和速度,同时减少计算负载.
主要方法:
- 开发了茶芽DSCF-YOLOv8n (TBF-YOLOv8n) 模型,通过使用分布式转移卷积 (DSConv) 修改两次卷积 (C2f) 的交叉阶段部分瓶模块.
- 集成协调注意力 (CA) 机制以减少干扰并提高平均精度.
- 实现了SIOU_Loss (SCYLLA-IOU_Loss) 和动态样本 (DySample) 的提升样本,以实现更快的融合和更好的检测性能.
主要成果:
- 与YOLOv8n相比,TBF-YOLOv8n实现了3.7%的精度增加和1.1%的平均精度增加.
- 该模型显示,千兆浮点操作 (GFLOPs) 减少了44.4%,参数减少了13.4%.
- 在检测准确度方面表现优于其他轻量级模型,同时保持较小的模型尺寸.
结论:
- TBF-YOLOv8n模型为智能茶叶收获提供了检测效率和精度之间的卓越平衡.
- 这项研究为推进自动茶叶采摘技术提供了宝贵的见解.
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