采用YOLO算法和无人机影像的松材线虫病异常变色木识别  

Recognition of abnormally discolored trees caused by pine wilt disease using YOLO algorithm and UAV images

作　　者：黄丽明 王懿祥[1,2,3] 徐琪 刘青华[4] Huang Liming;Wang Yixiang;Xu Qi;Liu Qinghua(College of Environment and Resource,Zhejiang A&F University,Hangzhou 311300,China;State Key Laboratory of Subtropical Silviculture,Zhejiang A&F University,Hangzhou 311300,China;Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration,Zhejiang A&F University,Hangzhou 311300,China;Research Institute of Subtropical Forestry,Chinese Academy of Forestry,Hangzhou 311300,China)

机构地区：[1]浙江农林大学环境与资源学院,杭州311300 [2]浙江农林大学,省部共建亚热带森林培育国家重点实验室,杭州311300 [3]浙江农林大学,浙江省森林生态系统碳循环与固碳减排重点实验室,杭州311300 [4]中国林科院亚热带林业研究所,杭州311300

出　　处：《农业工程学报》2021年第14期197-203,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：浙江省公益技术应用研究项目(LGF21C160001);浙江省重点研发计划项目(2020C02007)。

摘　　要：松材线虫病是一种传播速度快的毁灭性森林病害,利用无人机遥感及时对松材线虫病病害木进行监测,是控制松材线虫病蔓延的有效方式。该研究利用YOLO算法自动识别无人机遥感影像上的松材线虫病异常变色木,利用深度可分离卷积和倒残差结构改进YOLOv4算法,提高了识别的精度和效率。比较Faster R-CNN、EfficientDet、YOLOv4和YOLOv5与改进的YOLO算法的速度和精度,并分析了改进的YOLO算法在参与训练区域和未参与训练区域的异常变色木的识别效果。试验结果表明,改进后的YOLO算法的平均精度为80.85%,每个迭代周期的训练时间为164 s,参数大小为44.2 MB,单张影像的测试时间为17 ms,表现优于Faster R-CNN和YOLOv4,但与EfficientDet和YOLOv5相比有优有劣,综合比较这4个指标,改进算法在检测速度和检测精度上的表现更为平衡。未参与训练区域异常变色木的F1分数(84.18%)略低于参与训练区域(87.92%),但基本满足异常变色木的监测要求。相似地物、林分郁闭度、坡向和分辨率会对识别精度产生影响,但影响较小。因此,改进的YOLO算法精度高、效率高,可用于松材线虫病异常变色木的快速识别,并且对未参与训练区域异常变色木的识别具有较高的适用性。Pine wilt disease is a fast-spreading and destructive forest disease which can damage the entire pine forest in a short time.The key to control the disease is to identify abnormally discolored trees in a quick and accurate way.In this study,a fixed-wing Unmanned Aerial Vehicle(UAV)equipped with a professional true-color camera was used for image acquisition,and a deep learning algorithm—YOLO(You Only Look Once),was adopted to detect the images of abnormally discolored tree.The test was conducted on the platform using a RTX 2080 GPU with 8GB memory in the same parameters.The study area was divided into one training area and two testing areas,of which the training area and the testing area 1 were located in Xinjing Mountain,and the testing area 2 was located in Baota Mountain.In order to improve the efficiency and accuracy of the algorithm,depthwise separable convolution and inverted residual block were employed to improve YOLO algorithm,and the upward transmission of the low-level features of location of the Neck was removed.In order to verify the performance of the improved algorithm,the improved algorithm was compared with Faster R-CNN,EfficientDet,YOLOV4 and YOLOv5 in terms of accuracy precision,training time of per epoch,size of parameters and testing time of per image.Specifically,the accuracy precision of the improved YOLO algorithm increased to 80.85%,which was 1.25%,3.02%and 3.49%higher than Faster R-CNN,YOLOv4 and YOLOv5 respectively,and was 0.42%lower than EfficientDet;the training time of per epoch of the improved algorithm was 164 s,which was 187,172 and 115 s shorter than Faster R-CNN,EfficientDet and YOLOv4 respectively,and was 26 s longer than YOLOv5;the parameters size of the improved YOLO algorithm registered 44.23 MB,which was 477.35 and 199.69 MB smaller than Faster R-CNN and YOLOv4,and was 29.62 and 17.27 MB bigger than EfficientDet and YOLOv5;the testing time of per image of the improved YOLO algorithm decreased to 17 ms,which was 68,33 and 7 ms less than Faster R-CNN,EfficientDet and YOLOV4

关 键 词：无人机 深度学习 YOLO 松材线虫病 异常变色木 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]