基于YOLO_X和迁移学习的无人机影像玉米雄穗检测  

UAV images for detecting maize tassel based on YOLO_X and transfer learning

作　　者：汪斌斌 杨贵军 杨浩[2,3] 顾寄南[1] 赵丹[2,3] 许思喆 徐波 Wang Binbin;Yang Guijun;Yang Hao;Gu Jinan;Zhao Dan;Xu Sizhe;Xu Bo(School of Mechanical Engineering,Jiangsu University,Zhenjiang 212000,China;Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs,Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;School of Agricultural Engineering,Jiangsu University,Zhenjiang 212000,China)

机构地区：[1]江苏大学机械工程学院,镇江212000 [2]农业农村部农业遥感机理与定量遥感重点实验室,北京农业信息技术研究中心,北京100097 [3]国家农业信息化工程技术研究中心,北京100097 [4]江苏大学农业工程学院,镇江212000

出　　处：《农业工程学报》2022年第15期53-62,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划项目(2021YFD2000100,2019YFE0125300-1);广东省重点领域研发计划项目(2019B020216001)。

摘　　要：玉米雄穗表型信息的获取对研究玉米长势及产量起着非常重要的作用,为实现复杂田间环境玉米雄穗的精确识别和计数,该研究使用无人机采集试验田的玉米雄穗影像,基于FasterR-CNN、SSD、YOLO_X目标检测模型,使用迁移学习方法实现玉米雄穗的高精度识别,并分析了模型对不同品种和不同种植密度的玉米雄穗检测效果。试验结果表明,基于迁移学习的FasterR-CNN、SSD、YOLO_X的目标检测效果相比于未使用迁移学习的模型有明显提升,其中,迁移学习后YOLO_X的识别精确度为97.16%,平均精度为93.60%,准确度为99.84%,对数平均误检率为0.22,识别效果最好;不同玉米品种对模型的适应性有所差异,其中郑单958对模型适应性最好,Faster R-CNN、SSD、YOLO_X的决定系数R^(2)分别为0.9474、0.9636、0.9712;不同种植密度下玉米雄穗的检测效果有所差异,在29985,44978,67466,89955株/hm^(2)种植密度下,模型对郑单958检测的平均绝对误差分别为0.19、0.31、0.37、0.75,随着种植密度的增加,检测误差逐渐变大。研究为农田玉米雄穗高精度识别提供了一种可靠方法,对玉米表型性状高通量调查具有一定的应用价值。Maize tassels play a very important role in the process of maize growth.It is a high demand to realize the accurate identification and counting of maize tassels in the complex field environment.In this study,a complete detection and counting system was established for the farmland maize tassels using Unmanned Aerial Vehicle(UAV)remote sensing and computer vision,in order to promote the application of intelligent agriculture during maize production.The UAV images were also collected during the maize heading stage in the experimental field.Three target detection networks of Faster R-CNN,SSD,and YOLO_X were selected to realize the high-precision recognition of maize tassels using transfer learning.Specifically,the UAV was firstly utilized to collect the RGB images of maize tassels with a height of 10 m on August 9,2021.Secondly,the UAV images of maize tassels were cut into 600×600 pixels.The same number of samples were then selected for the training set,verification set,and test set,according to each variety and planting density.Finally,the weight of training on the public dataset was transferred to the target model using transfer learning.The recognition performance of maize tassel was compared before and after transfer learning.The experimental results show that the average precision,the recall rate,and the accuracy rate of Faster R-CNN target detection networks increased by 16.41,21.86,and 10.01 percentage points,respectively,compared with the SSD,and YOLO_X.By contrast,the average precision,recall rate,and accuracy rate of the SSD increased by 3.05,1.76 percentage points,respectively.The average precision,the recall rate,and the accuracy rate of YOLO_X increased by 3.56,4.51 percentage points,respectively.Among them,the recognition precision,average precision,accuracy,and LAMR of YOLO_X after transfer learning reached 97.16%,93.60%,99.84%,and 0.22,respectively,compared with the Faster R-CNN and SSD networks.The best performance was achieved for the detection of maize tassel.In addition,the Faster R-CNN,SSD,and

关 键 词：无人机 目标检测 图像识别 玉米雄穗 迁移学习 深度学习 

分 类 号：S513[农业科学—作物学] TP391[自动化与计算机技术—计算机应用技术]