YOLOv8-FDA:基于改进的YOLOv8的轻量级小麦耳朵检测和无人机图像中的计数
Yuxuan Lin1, Xiao Xiao1, Haifeng Lin1
1College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.
Frontiers in plant science
|October 10, 2025
概括
这项研究介绍了YOLOv8-FDA,这是一种轻量级的人工智能模型,用于精确地检测和计数小麦耳朵. 它在复杂的田间条件下达到高精度,有助于农业产量预测和管理.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的小麦耳检测对于作物产量预测和管理至关重要.
- 现场复杂性为开发轻量级,高精度检测系统带来了挑战.
研究的目的:
- 开发一种轻量级,高精度的小麦耳检测和计数方法.
- 提高农业监测系统的效率和准确性.
主要方法:
- 提出了YOLOv8-FDA,这是一个基于YOLOv8.8的轻量级检测模型.
- 集成的RFAConv用于特征提取,Dysample用于上采样,HWD用于模型压缩,SDL损失用于界限框回归.
主要成果:
- 在GWHD数据集上,YOLOv8-FDA实现了86.3%的精度,77.5%的回忆率和84.9%的mAP@0.5.
- 该模型的尺寸为2.96MB,8.3 GFLOPs,运行速度为19.2 FPS.
- 通过交叉行细分实现了超过97.5%的准确度的实时计数.
结论:
- YOLOv8-FDA表现出强大的检测性能和轻量特性.
- 该模型提供了有效的实时功能,适合实际农业应用.
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