Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN  

作　　者：Heming Hu Yutaka Kaizu Hongduo Zhang Yongwei Xu Kenji Imou Ming Li Jingjing Huang Sihui Dai 

机构地区：[1]Graduate School of Agricultural and Life Sciences,the University of Tokyo,Tokyo 113-0033,Japan [2]Hunan Agricultural Equipment Research Institute,Changsha,Hunan 410125,China [3]College of Horticulture,Hunan Agricultural University,Changsha,Hunan 410128,China

出　　处：《International Journal of Agricultural and Biological Engineering》2022年第6期175-179,共5页国际农业与生物工程学报（英文）

摘　　要：To solve the problem of high labour costs in the strawberry picking process,the approach of a strawberry picking robot to identify and find strawberries is suggested in this study.First,1000 images including mature,immature,single,multiple,and occluded strawberries were collected,and a two-stage detection Mask R-CNN instance segmentation network and a one-stage detection YOLOv3 target detection network were used to train a strawberry identification model which classified strawberries into two categories:mature and immature.The accuracy ratings for YOLOv3 and Mask R-CNN were 93.4%and 94.5%,respectively.Second,the ZED stereo camera,triangulation,and a neural network were used to locate the strawberry in three dimensions.YOLOv3 identification accuracy was 3.1 mm,compared to Mask R-CNN of 3.9 mm.The strawberry detection and positioning method proposed in this study may effectively be used to supply the picking robot with a precise location of the ripe strawberry.

关 键 词：strawberry detection 3D point cloud MEAN-SHIFT clustering method 

分 类 号：S668.4[农业科学—果树学]