[1]王西志,管声启,张理博,等.基于视觉引导的工业棒材上料系统研究[J].机械与电子,2023,41(05):19-23.
　WANG Xizhi,GUAN Shengqi,ZHANG Libo,et al.Research on Industrial Bar Feeding System Based on Visual Guidance[J].Machinery & Electronics,2023,41(05):19-23.
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基于视觉引导的工业棒材上料系统研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
41
期数:
2023年05期
页码:
19-23
栏目:
设计与研究
出版日期:
2023-05-25

文章信息/Info

Title:
Research on Industrial Bar Feeding System Based on Visual Guidance
文章编号:
1001-2257 ( 2023 ) 05-0019-05
作者:
王西志; 管声启; 张理博; 刘 通; 郝振虎
西安工程大学机电工程学院,陕西 西安 710048
Author(s):
WANG Xizhi ;  GUAN Shengqi ;  ZHANG Libo ;  LIU Tong ;  HAO Zhenhu
( School of Mechanical and Electronic Engineering , Xi ’an Polytechnic University , Xi ’an 710048 , China )
关键词:
上料系统; 深度学习; 位姿检测; 两指机械手
Keywords:
feeding system ;  deep learning ;  pose detection ;  two finger manipulat
分类号:
TP391.4 ; TP241
文献标志码:
A
摘要:
为了提高生产效率,设计一种基于视觉引导的工业棒材上料系统。首先,为了实现视觉引导进行工业棒材上料,设计了工业棒材上料总体方案,并对上料机械结构模型进行选型设计。然后,为了实现棒材的自动 识别和位姿 检测,提出 了一种基于改进 YOLOv5 的旋转目标 识别与定位 算法。该方法在 YOLOv5 主干特征网络上,添加高效 ECA 通道注意力机制模块,利用其避免降维,并通过适当跨通道交互策略提高特征提取能力;为了增强不同尺度的特征信息融合,将原特征增强网络替换成 BiFPN 加权双向特征金字塔网络,进行自上而下和自下而上的多尺度特征融合,提高棒材识别准确率并获取平面位置信息;在此基础上,采用双目视觉进行立体匹配获取棒材的深度位置信息,最终实现棒材立体位姿检测。对所提上料系统进行实验验证,棒材识别的平均精度为 99.4% ,抓取棒材成功率达到 90% 及以上。
Abstract:
In order to improve the production efficiency , this paper designs an industrial bar feeding system based on visual guidance.First of all , in order to realize the visual guidance of industrial bar feeding , the overall scheme of industrial bar feeding is designed , and the selection and design of the feeding mechanical structure model are designed.Then , in order to realize automatic bar recognition and pose detection , a rotating target recognition and location algorithm based on improved YOLOv5 is proposed.In this method , an efficient ECA channel attention mechanism module is added to the YOLOv5 backbone feature network , and the feature extraction ability is improved by using its avoidance reduction and appropriate cross-channel interaction strategy.In order to enhance the feature information fusion of different scales , the original feature enhancement network was replaced with BiFPN weighted bidirectional feature pyramid network , and the top-down and bottom-up multi-scale feature fusion was carried out to improve the bar recognition accuracy and obtain the plane position information.On this basis , the depth and position information of the bar is obtained by stereo matching with binocular vision , and finally the stereo pose detection of the bar is realized.Through the experimental verification of the feeding system in this paper , the average accuracy of bar recognition is 99.4% , and the success rate of grasping bar is 90% or above.

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备注/Memo

备注/Memo:
收稿日期: 2022-10-11
基金项目:西安市创新能力强基计划 人工智能技术攻关项目( 21RGZN0021 )
作者简介:王西志 ( 1998- ),男,河北邢台人,硕士研究生,研究方向为机器人视觉;管声启 ( 1971- ),男,安徽安庆人,博士,教授,研究方向为机械零件质量检测、机器人视觉等。
更新日期/Last Update: 2023-05-23