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

查看全文 作　　者：Heming [1]Hu;Yutaka [1]Kaizu;Hongduo [1]Zhang;Yongwei [1]Xu;Kenji [1]Imou;Ming [2]Li;Jingjing [2]Huang;Sihui [3]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》索引2022年第15卷第6期,共5页

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

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

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