一种轻量级改进的YOLOv8方法,用于智能检测松病
Gang Chen1, Mengquan Wu2,3, Longxing Liu4
1College of Resources and Environmental Engineering, Ludong University, Yantai, 264039, China.
Scientific reports
|November 20, 2025
概括
早期检测松木线虫病 (PWD) 是至关重要的. 一种新的智能检测模型,PWD-YOLO-D,使用无人机图像和深度学习来更准确,更有效地识别受感染的松树.
科学领域:
- 森林病理学 森林病理学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 松树线虫病 (PWD) 对松树林构成重大全球威胁,需要改进检测方法.
- 目前对PWD的监测技术缺乏有效的疾病管理所需的效率和精度.
- 早期和准确的识别对于控制PWD传播和尽量减少森林损害至关重要.
研究的目的:
- 用无人机遥感和深度学习开发PWD的智能检测模型.
- 为了提高检测感染PWD的松树的准确性和效率.
- 为及时干预和管理PWD提供一个强大的工具.
主要方法:
- 介绍了PWD-YOLO-D,这是一个基于YOLOv8深度学习框架和无人机遥感图像的智能检测模型.
- 集成一个高效的多尺度交叉注意力 (EMCA) 机制,以改善特征表示.
- 整合了自组装注意力模块 (SEAM) 和焦点器-IoU损失功能,以提高检测稳定性和定位准确性.
主要成果:
- 与原来的YOLOv8.8相比,PWD-YOLO-D模型表现出优越的性能.
- 在AP@0.5实现了4.0%的增长,在AP@0.5:0.95.5增长了7.3%.
- 减少了0.48 MB的模型参数,同时改善了检测指标.
结论:
- 该PWD-YOLO-D模型在自动检测PWD感染松树方面取得了重大进展.
- 该模型的提高准确性和效率为森林健康监测和管理提供了至关重要的支持.
- 这种深度学习方法有助于及时和精确地干预像PWD这样的破坏性森林疾病.
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