林业工程学报2025，Vol.10Issue(2)：147-155,9.DOI:10.13360/j.issn.2096-1359.202401018
基于改进YOLOv7的航拍图像下松材线虫病疫木识别
Improved YOLOv7 based aerial images of pine nematode epidemic wood recognition
摘要
Abstract
Pine nematode disease is a highly harmful infectious disease of pine caused by pine wood nematode,which causes serious losses to forestry resources in China.To accurately comprehend the quantity and distribution of pine nematode infected trees in a large-scale range,this study proposed a model for detecting pine wilt infected trees under UAV aerial images based on the improved YOLOv7 algorithm.For the problem of wrong detection and omission of infected trees caused by the complex background of aerial images,this study introduced the SimAM attention mechanism in the main feature extraction part,so that the model can better focus on key features such as color,texture,and other key features of the pine nematode infected trees.Secondly,the ELAN-W network in the Head part was replaced with ConvNeXt network,which improved the efficiency of the model for single infected tree feature extraction,and reduced the number of model parameters and improved the model detection speed.Then,SPD-Conv was introduced to improve the detection accuracy and recall rate of small targets in low resolution aerial images.Finally,the convolution of the neck network was replaced by CoordConv to better feel the position information of the epidemic trees in the feature map.The experiment result showed that the improved YOLOv7 model could effectively detect the pine nematode infected trees,with a precision of 91.1%,a recall rate of 93.5%,and an F1 score of 92.3%,which were 1.6,3.4,and 2.5 percentage points higher than the original YOLOv7 model,respectively.Compared with other current mainstream models,the improved model showed some improvement in all the main indexes.In addition,in the ideal confidence threshold range,the precision,recall rate and F1 score of the improved model were always better than the original model for the detection of two test plots with different degrees of disaster.The above experimental results showed that the improved model had good adaptability and can be effectively applied in large-scale pine nematode epidemic tree survey,which had positive significance for the prevention and control of pine wood nematodes and the protection of forestry resources.关键词
松材线虫病/大尺度范围疫木识别/无人机航拍图像/目标检测/YOLOv7Key words
pine nematode disease/large-scale range recognition of epidemic wood/UAV aerial images/object detection/YOLOv7分类
信息技术与安全科学引用本文复制引用
胡丹妮,吴红玉,叶振..基于改进YOLOv7的航拍图像下松材线虫病疫木识别[J].林业工程学报,2025,10(2):147-155,9.基金项目
浙江省自然科学基金探索项目(LTGN23F020001). （LTGN23F020001）
