YOLO-LF:在农业疾病检测中应用多尺度信息融合和小目标检测
Xinming Wang1, Sai Hong Tang1, Mohd Khairol Anuar B Mohd Ariffin1
1Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
Frontiers in plant science
|September 29, 2025
概括
这项研究引入了一种改进的YOLO-LF模型,用于自动化农业疾病检测. 该模型增强了复杂背景中的小目标识别和病变识别,大大提高了检测准确度.
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:
- 传统的手动作物疾病检测是低效和不可靠的.
- 现有的深度学习模型在复杂的农业背景下与小目标和多层次损伤作斗争.
研究的目的:
- 开发一个改进的深度学习模型,用于准确有效地自动检测农业疾病.
- 提高在复杂的背景中检测小目标和多尺度病变的性能.
主要方法:
- 提出了一个改进的YOLO-LF模型,包括CSPPA (跨阶段部分与金字塔注意),SEA (SeaFormer注意) 和LGCK (本地高斯卷积内核) 模块.
- CSPPA模块用于增强的多尺度特征融合.
- 为了更好地关注上下文和本地信息,SEA模块.
- 增加对小损伤区域敏感性的LGCK模块.
主要成果:
- YOLO-LF模型在2020年和2021年植物病理学数据集上显示了显著的性能改进.
- 与现有的主流模型相比,在mAP@0.5%和mAP@0.5-0.95%实现了优异的性能.
- 在农业疾病识别中有效处理复杂的背景和小目标检测挑战.
结论:
- 拟议的YOLO-LF模型为自动化农业疾病检测提供了一个高度有效的解决方案.
- 集成新的注意力和卷积模块显著提高了检测准确性和效率.
- 该方法在现实世界农业应用中具有很高的实用价值.
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