福建林业科技2026，Vol.53Issue(2)：66-74,81,10.DOI:10.13428/j.cnki.fjlk.2026.02.008
基于多核特征融合感知的林区枯死木检测方法
Multi-Core Feature Fusion Perception based Dead Wood Detection Method in Forest Areas
祖赣1
作者信息
- 1. 顺昌县数字林业信息中心,福建 南平 353200
- 折叠
摘要
Abstract
Pine Wilt Disease(PWD)is a devastating disease that severely harms pine forest ecosystems.It spreads rapidly and is dif-ficult to control,while traditional manual monitoring methods are inefficient and costly.To address this,an improved lightweight ob-ject detection network,M-YOLOv8n-p2,is proposed based on drone aerial image data.By introducing a multi-kernel feature fusion perception mechanism,the model's ability to distinguish features of dead pine trees is effectively enhanced.Compared with existing YOLO improvement methods,while maintaining lightweight,it significantly enhances the extraction and fusion efficiency of multi-scale features,effectively solving the problem of missed and false detections of small targets in complex backgrounds.The proposed network has higher accuracy and robustness in the detection task of dead trees caused by Pine Wilt Disease,enabling efficient and accurate identification of diseased trees.It can provide reliable technical support for intelligent monitoring and prevention and control of forestry pests and diseases.关键词
松材线虫病/机器学习/监测方法Key words
pine wilt disease/machine learning/monitoring method分类
农业科技引用本文复制引用
祖赣..基于多核特征融合感知的林区枯死木检测方法[J].福建林业科技,2026,53(2):66-74,81,10.
