基于Halcon的仓库机器人托盘检测

F. Jia, Zhaosheng Tao, Fusong Wang
{"title":"基于Halcon的仓库机器人托盘检测","authors":"F. Jia, Zhaosheng Tao, Fusong Wang","doi":"10.1109/AIID51893.2021.9456540","DOIUrl":null,"url":null,"abstract":"Pallet detection is the key step of cargo handling for warehouse robots. In order to improve the recognition rate of pallet detection due to the influence of complex background, a pallet detection method based on point cloud is proposed. In this method, time-of-flight (ToF) camera is used to collect the point cloud. ResNet50 neural network model which is provided by Halcon software is used for deep learning, and deep learning is used to extract the region of interest of pallet contour. The extracted region of interest is processed to obtain the regions of the pallet pockets, and the minimum rectangles surrounding each of the pocket regions are solved to obtain the position coordinates of the pocket centers. The experimental results show that the precision of pallet detection can reach 94.5%. This method has high recognition rate in complex background, and has reference value for the design of pallet detection system of warehouse robots.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Pallet Detection Based on Halcon for Warehouse Robots\",\"authors\":\"F. Jia, Zhaosheng Tao, Fusong Wang\",\"doi\":\"10.1109/AIID51893.2021.9456540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Pallet detection is the key step of cargo handling for warehouse robots. In order to improve the recognition rate of pallet detection due to the influence of complex background, a pallet detection method based on point cloud is proposed. In this method, time-of-flight (ToF) camera is used to collect the point cloud. ResNet50 neural network model which is provided by Halcon software is used for deep learning, and deep learning is used to extract the region of interest of pallet contour. The extracted region of interest is processed to obtain the regions of the pallet pockets, and the minimum rectangles surrounding each of the pocket regions are solved to obtain the position coordinates of the pocket centers. The experimental results show that the precision of pallet detection can reach 94.5%. This method has high recognition rate in complex background, and has reference value for the design of pallet detection system of warehouse robots.\",\"PeriodicalId\":412698,\"journal\":{\"name\":\"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIID51893.2021.9456540\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIID51893.2021.9456540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

托盘检测是仓库机器人货物搬运的关键环节。为了提高受复杂背景影响的托盘检测识别率,提出了一种基于点云的托盘检测方法。该方法采用飞行时间(ToF)相机采集点云。利用Halcon软件提供的ResNet50神经网络模型进行深度学习,利用深度学习提取托盘轮廓感兴趣区域。对提取的感兴趣区域进行处理,得到托盘袋的区域,并求解每个袋区域周围的最小矩形,得到袋中心的位置坐标。实验结果表明,该方法对托盘的检测精度可达94.5%。该方法在复杂背景下具有较高的识别率,对仓库机器人托盘检测系统的设计具有参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pallet Detection Based on Halcon for Warehouse Robots
Pallet detection is the key step of cargo handling for warehouse robots. In order to improve the recognition rate of pallet detection due to the influence of complex background, a pallet detection method based on point cloud is proposed. In this method, time-of-flight (ToF) camera is used to collect the point cloud. ResNet50 neural network model which is provided by Halcon software is used for deep learning, and deep learning is used to extract the region of interest of pallet contour. The extracted region of interest is processed to obtain the regions of the pallet pockets, and the minimum rectangles surrounding each of the pocket regions are solved to obtain the position coordinates of the pocket centers. The experimental results show that the precision of pallet detection can reach 94.5%. This method has high recognition rate in complex background, and has reference value for the design of pallet detection system of warehouse robots.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信