基于深度视觉的智能卸垛机快递箱边界检测系统
Boundary detection system of express carton for intelligent depalletizer based on deep vision
黄辉城 1李建新2
作者信息
- 1. 广州市公用事业技师学院社会服务系,广州 510100
- 2. 东莞职业技术学院电子信息学院,广东东莞 523808
- 折叠
摘要
在智慧物流领域中,智能卸垛机利用视觉传感器采集的信息来指导机械臂抓取托盘上堆叠的快递箱,精确的快递箱边界检测是决定机械臂抓取点性能的关键.快递箱的无序分布、快递标签与印刷图文对快递箱边界检测的精度造成不利的影响,针对此问题,提出一种基于深度视觉的快递箱边界检测方法.首先,基于深度图像计算快递箱的粗粒度边界,从而增强快递箱边界的检测能力,并提高对快递箱姿态的识别能力.然后,将粗粒度边界图与RGB图像的融合图像输入U-Net网络进行语义分割,U-Net网络使用水平集损失与交叉熵损失的混合损失函数进行训练,以提高快递箱边界检测的精度.实验结果表明,所提方法提高了快递箱边界检测的精度,检测误差可降至7mm以下.
Abstract
In the field of smart logistics,the intelligent depalletizer takes advantage of information acquired by vision sensors to guide machine arm to grab the express box on the tray,so the accurate express box boundary detection is cruci-al to the grab point performance of the machine arm.The disorderly distribution、express label and printed content of the express boxes have an adverse effect on the detection precision of express box boundary,aiming at this problem,a ex-press box boundary detection method based on deep vision is proposed.Firstly,the coarse-grained boundary of the ex-press box is computed based on the depth image,thus the boundary detection ability of the express box is enhanced,the pose recognition ability of the express box is improved as well.Then,the coarse-grained boundary image and RGB image are fused to as the input of U-Net model to perform semantic segmentation,the U-Net model is trained with a mixed loss function combining level set loss and cross entropy loss,in order to improve the detection precision of the express boxes.Experimental results show that the proposed method improves the boundary detection precision of the express box,and the detection error is below 7 millimeter.
关键词
智慧物流/智能分拣系统/快递箱卸跺/深度视觉/深度神经网络/机械臂Key words
intelligent logistics/intelligent sorting system/express carton depalletize/deep vision/deep neural net-work/mechanical arm引用本文复制引用
基金项目
广东省高校科研平台重点领域专项(2021ZDZX1093)
东莞市科技特派员项目(20231800500282)
东莞社会科技发展项目(20231800903592)
东莞社会科技发展项目(20211800900252)
出版年
2024
NETL
NSTL
万方数据