棉花田的轻量级杂草检测模型,基于改进的YOLOv8n
Jun Wang1, Zhengyuan Qi2, Yanlong Wang2
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China. julianwong82@163.com.
Scientific reports
|January 2, 2025
概括
一个新的轻量级深度学习模型,YOLO-Weed Nano,有效地检测棉花田中的杂草. 这种模型为实际农业应用提供了更高的准确性和显著减少的计算资源.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 在棉花田中杂草的扩散显著影响作物产量和健康.
- 深度学习模型提供高精度的杂草识别,但往往受到高计算需求和资源消耗的影响.
- 对于有效和轻量级的杂草检测方法,迫切需要在农业中实际实施.
研究的目的:
- 开发一种高效,轻量级的深度学习算法,用于检测棉花田中的杂草.
- 在计算成本和资源使用方面解决现有的复杂深度学习模型的局限性.
- 提高农业中自动杂草识别系统的实际应用.
主要方法:
- 提出了YOLO-Weed纳米算法,这是YOLOv8n模型的优化版本.
- 集成深度可分离卷积 (DSC) 进入HGNetV2网络 (DS_HGNetV2) 作为模型的骨干.
- 集成的双向特征金字塔网络 (BiFPN) 用于增强特征融合,以及轻量级的LiteDetect头用于减少计算.
主要成果:
- 与原始YOLOv8n模型相比,YOLO-Weed Nano在平均平均精度 (mAP) 中表现出1%的改进.
- 拟议的模型实现了参数 (63.8%),计算 (42%) 和权重 (60.7%) 的显著减少.
- 这些优化使该模型更适合在资源有限的农业环境中部署.
结论:
- YOLO-Weed纳米算法为棉花田杂草检测提供了一个高效和轻量级的解决方案.
- 该模型成功地平衡了高检测精度与降低计算复杂度.
- 这一进步促进了深度学习的实际应用,以便在现代农业中有效地管理杂草.
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