BRA-YOLOv7:在YOLOv7中使用FasterNet和双层路由注意力来改进大叶病对象检测
Rong Ye1,2, Quan Gao2,3, Tong Li2,3
1College of Food Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China.
Frontiers in plant science
|December 24, 2024
概括
一个新的BRA-YOLOv7模型在复杂的云南环境中改善了茶叶疾病的检测. 这种增强的物体检测系统为茶叶生产中的疾病识别提供了更高的准确性和效率.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 茶叶疾病显著降低作物产量和质量.
- 在云南复杂的茶叶种植环境中检测疾病是具有挑战性的,原因是小的,分散的病变,遮蔽和不同的照明.
- 现有的物体检测模型缺乏有效识别茶叶疾病所需的准确性.
研究的目的:
- 在复杂的农业环境中开发一种精确有效的茶叶病物检测模型.
- 改进现有的YOLOv7架构,以增强疾病识别和定位.
主要方法:
- 拟议的BRA-YOLOv7模型,以PConv和FasterNet取代YOLOv7的骨干,以提高效率.
- 在Neck层中集成了一种双层路由动态稀疏注意力机制,以改善全球信息捕获.
- 用MPDIoU取代损失函数,以提高本地化准确度并减少错误检测.
主要成果:
- 与传统的YOLOv7.7相比,BRA-YOLOv7在识别精度上实现了4.8%的改进,平均精度 (mAP) 提高了2.6%.
- 该模型显示,浮点运算 (FLOP) 减少了1.4G,每秒 (FPS) 提高了5.52%.
- 在召回率上实现了5.3%的改善,在平衡得分上实现了5%的改善.
结论:
- BRA-YOLOv7模型在参数数量,FLOP,模型大小和收时间方面提供了显著的改进.
- 提供高效,无损的茶叶疾病的识别,平衡准确性,实时性能和稳定性.
- 允许在农业应用中对茶叶疾病采取更有效的有针对性的预防措施.
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