计算机工程与应用2024，Vol.60Issue(10)：209-216,8.DOI:10.3778/j.issn.1002-8331.2301-0163
基于图像实例分割的机器人箱体拆垛方法
Robot Box Depalletizing Method Based on Image Instance Segmentation
邹汶材 1刘宝临2
作者信息
- 1. 西南交通大学 计算机与人工智能学院,成都 610000
- 2. 西南交通大学 信息科学与技术学院,成都 610000
- 折叠
摘要
Abstract
In order to solve the problem that the traditional feature extraction method in the unstacking task of industrial box parcels depends on the shape of the box and is difficult to apply to variable specifications and mixed box types,a robot unstacking method based on image instance segmentation is proposed.In order to obtain accurate box picking center,the Mask R-CNN is used to perform instance segmentation to obtain the box mask and classification information,and an STN(spatial transformation network)module is added after feature extraction to optimize the recognition of rotating objects.Then,the minimum circumscribed rectangle of the mask is solved and post-processed to obtain the pixel center and horizontal rotation angle of the box to be picked,and a unstacking strategy is combined to sort first row and then column to determine the picking order.Finally,the calibration method is used to complete the box positioning,and the segmentation and robot unstacking experiments are conducted for variable specifications and mixed box stacking types.The results show that the average pixel distance between the pixel centers of the unboxed body is about 4 pixels,and the average error of spatial location is about 1 cm.The position accuracy meets the actual needs of industrial depalletization.关键词
实例分割/Mask R-CNN/分类/标定/工业拆垛Key words
instance segmentation/Mask R-CNN/classification/calibration/industrial depalletization分类
信息技术与安全科学引用本文复制引用
邹汶材,刘宝临..基于图像实例分割的机器人箱体拆垛方法[J].计算机工程与应用,2024,60(10):209-216,8.
