An object detection method for bayberry trees based on an improved YOLO algorithm  

作　　者：Youliang Chen Hanli Xu Xiangjun Zhang Peng Gao Zhigang Xu Xiaobin Huang 

机构地区：[1]School of Civil and Surveying Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,People’s Republic of China [2]School of Resource Engineering,Longyan University,Longyan,People’s Republic of China [3]The Engineering&Technical College,Chengdu University of Technology,Leshan,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期781-805,共25页国际数字地球学报（英文）

基　　金：supported by the Fujian Science and Technology Program(grant numbers 2021Y0074);the Fujian Science and Technology Innovation Fund(grant numbers 2020SHQM14;XIQM009).

摘　　要：To quickly detect and count the number of bayberry trees,this paper improves the YOLO-v4 model and proposes an optimal YOLO-v4 method for detecting bayberry trees based on UAV images.We used the Leaky_ReLU activation function to accelerate the model extraction speed and used the DIoU NMS to retain the most accurate prediction boxes.In order to increase the recall rate of the object detection and construct the optimal YOLO-v4 model,the K-Means clustering method was embedded into DIoU NMS.We trained the model using UAV images of bayberry trees,it was determined that the optimal YOLO-v4 model threshold was 0.25,which had the best extraction effect.The optimal YOLO-v4 model had a detection accuracy of up to 97.78%and a recall rate of up to 98.16%on the dataset.The optimal YOLO-v4 model was compared with YOLO-v4,YOLO-v4 tiny,the YOLO-v3 model,and the Faster R-CNN model.With guaranteed accuracy,the recall rate was higher,up to 97.45%,and the detection of bayberry trees was better in different contexts.The result shows that the optimal YOLO-v4 model can accurately achieve the rapid detection and statistics of the number of bayberry trees in large-area orchards.

关 键 词：Object detection YOLO-v4 deep learning bayberry trees plant number statistics 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]