YOLO-PEST:一种基于YOLOv5s的新型大米病虫检测方法
Jun Qiang1, Li Zhao2, Hongming Wang2
1School of Computer and Information, Anhui Polytechnic University, Beijing Middle Road, Wuhu, Anhui, 241000, China. chiang_j@ahpu.edu.cn.
Plant methods
|September 26, 2025
概括
本研究介绍了YOLO-PEST用于准确检测水害虫,改善智能农业中小物体识别和遮处理. 该模型实现了97%的mAP@0.5,提高了害虫管理系统.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 精确的水害虫检测对于智能农业系统至关重要.
- 挑战包括有限的数据集,害虫遮蔽和小物体检测准确度差.
研究的目的:
- 开发一种创新的检测方法,YOLO-PEST,以克服目前在水害虫监测方面的局限性.
- 提高害虫检测模型的准确性和稳定性.
主要方法:
- YOLO-PEST使用YOLOv5s架构与集成的ConvNeXt模块进行多尺度特征提取.
- 整合了CoTAttention机制,以提高复杂环境中的稳定性.
- 数据增强包括随机裁剪以模拟害虫封闭.
主要成果:
- YOLO-PEST的平均平均精度 (mAP@0.5) 达到了97%.
- 这比以前的方法改进了1.4个百分点.
- 该模型在检测小型和封闭的水害虫方面表现出更高的准确性.
结论:
- YOLO-PEST有效地解决了在水害虫检测方面的挑战,包括遮和小物体识别.
- 提出的方法显著提高了检测性能,为智能农业系统提供了有价值的工具.
- 集成ConvNeXt和CoTAttention可以提高模型的准确性和稳定性.
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