基于无人机影像和改进YOLOv11算法的松材线虫病变色疫木检测OA
Detection of discolored trees infected by pine wilt disease based on improved YOLOv11 algorithm and UAV imagery
为提升基于深度学习的松材线虫病变色疫木检测精度,以YOLOv11为基础算法,将小波卷积(wavelet transform convolution,WTConv)与三重注意力(triplet attention)机制相结合,构建改进的YOLOv11算法,基于无人机可见光遥感影像,将算法应用于浙江省缙云县松材线虫病变色疫木检测中,对比YOLOv8、YOLOv11和改进的YOLOv11算法对松材线虫病感染木和枯死木的检测效果.结果表明:改进后的YOLOv11算法具有更好的性能,检测平均精度均值达97.7%,精确率达97.4%,召回率为95.4%,均优于YOLOv8和YOLOv11算法;在未训练区域,松材线虫病感染木的F1分数为94.4%,改进的YOLOv11算法模型在未参与训练的区域仍然能准确地识别目标.研究结果提供了一种精度更高的松材线虫病变色疫木检测算法,为松材线虫病变色疫木定位提供了更加准确的工具支持.
To improve the detection accuracy of pine wilt disease based on deep learning,an improved YOLOv11 algorithm was constructed by combining wavelet convolution(WTConv)with triplet attention mechanism(triplet attention)based on YOLOv11 algorithm.Based on UAV visible light remote sensing image,the algorithm was applied to the detection of pine wilt disease in Jinyun county,Zhejiang province.The detection effects of YOLOv8,YOLOv11 and improved YOLOv11 algorithm on infected and dead trees caused by pine wilt disease were compared.The results showed that the improved YOLOv11 algorithm had better performance.The average detection accuracy was 97.7%,the precision was 97.4%,and the recall was 95.4%,which were better than YOLOv8 and YOLOv11 algorithms.In the untrained area,the F1 score of pine wilt disease infected tree was 94.4%,and the improved YOLOv11 algorithm model could still accurately identify the target in the untrained area.The research results provided a more accurate detection algorithm for pine wilt disease,and provided more accurate tool support for the location of pine wilt disease.
陈筱涵;陈广生;周均瑞;陈利杰;刘闯;陈潇扬
浙江农林大学环境与资源学院、碳中和学院,浙江 杭州 311300||缙云县林业局,浙江 缙云 321402浙江农林大学环境与资源学院、碳中和学院,浙江 杭州 311300缙云县林业局,浙江 缙云 321402缙云县林业局,浙江 缙云 321402缙云县林业局,浙江 缙云 321402缙云县林业局,浙江 缙云 321402
农业科技
松材线虫病变色疫木YOLO算法无人机影像目标检测
pine wilt diseasediscolored epidemic treeYOLO algorithmUAV imagetarget detection
《中国森林病虫》 2026 (1)
24-33,10
政府间国际科技创新合作重点研发计划项目"典型森林生态系统韧性调控机制与适应性管理"(2023YFE0105100)
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