Point cloud segmentation method of trees based on improved DGCNN
[Objectives]The object segmentation is a fundamental vision task for orchard sprayers to achieve precision spraying of trees by providing their phenotypic characteristics.In the kind of task,the trees planting in the nursery can be segmented with different parts including crowns,trunks and so on,which can provide perceptual information to the sprayer and help them realize the accurate spraying or navigation operation in the orchard or nursery scenery without manual intervention.Comparing to 2D images,point clouds are more suitable for being employed in some outside environments like orchards and nurseries,as point clouds enable the sprayer to acquire 3D structures of trees and are independent from illumination.[Methods]In this study,we introduced TSNet,a point cloud segmentation network with smaller module size that can be easily deployed on orchard sprayer.The network mainly has the following advantages:1)The network can realize the segmentation task of tree point clouds,which improves based on DGCNN.2)The introduction of gn Conv based continuous recursive gate convolution(gConv)operation is built to improve the effectiveness of segmentation in trees.3)The weight channel is designed to avoid loss of global features caused by process of deep mining.[Results]The mIoU value of TSNet tree segmentation reached 90.08％,and the number of model size was 0.72 M,which was better than the commonly used point cloud segmentation algorithms:PointNet,PointNet++,DGCNN,CurveNet,PointMLP,and D-PointNet++.[Conclusions]The proposed method can provide more accurate perceptual information for nursery tree detection and identification and agricultural robot operation.
point cloudtree segmentationdeep learningprecision sprayingorchard robot
万方数据
维普
