• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种结合深度学习目标检测和空间关系用于地理可视化的移动户外增强现实方法。

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.

作者信息

Rao Jinmeng, Qiao Yanjun, Ren Fu, Wang Junxing, Du Qingyun

机构信息

School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.

Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.

出版信息

Sensors (Basel). 2017 Aug 24;17(9):1951. doi: 10.3390/s17091951.

DOI:10.3390/s17091951
PMID:28837096
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5621345/
Abstract

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device's built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

摘要

本研究的目的是开发一种强大、快速且无标记的移动增强现实方法，用于在不受控制的户外环境中进行配准、地理可视化和交互。我们提出了一种用于移动或嵌入式设备的基于深度学习的轻量级目标检测方法；该方法基于视觉的检测结果通过主机设备内置的全球定位系统接收器、惯性测量单元和磁力计与空间关系相结合。基于地理空间信息生成的虚拟对象被精确地注册到现实世界中，并实现了一种基于触摸手势的交互方法。整个方法独立于网络，以确保在信号条件不佳时的鲁棒性。开发了一个原型系统并在武汉大学校园进行了测试，以评估该方法并验证其结果。研究结果表明，我们的方法实现了高检测精度、稳定的地理可视化结果和交互。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/7e83b70f3604/sensors-17-01951-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/1544f7282477/sensors-17-01951-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/ada0dcf40c47/sensors-17-01951-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/841fe00d1364/sensors-17-01951-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/11a7eaefc286/sensors-17-01951-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/dcdf203293c3/sensors-17-01951-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/223d8a29819c/sensors-17-01951-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/9bcdadf7c53b/sensors-17-01951-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/b7f97c2efe4f/sensors-17-01951-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/7e83b70f3604/sensors-17-01951-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/1544f7282477/sensors-17-01951-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/ada0dcf40c47/sensors-17-01951-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/841fe00d1364/sensors-17-01951-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/11a7eaefc286/sensors-17-01951-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/dcdf203293c3/sensors-17-01951-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/223d8a29819c/sensors-17-01951-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/9bcdadf7c53b/sensors-17-01951-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/b7f97c2efe4f/sensors-17-01951-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d1/5621345/7e83b70f3604/sensors-17-01951-g009.jpg

相似文献

1
A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.一种结合深度学习目标检测和空间关系用于地理可视化的移动户外增强现实方法。
Sensors (Basel). 2017 Aug 24;17(9):1951. doi: 10.3390/s17091951.
2
An augmented reality tool for learning spatial anatomy on mobile devices.一种用于在移动设备上学习空间解剖学的增强现实工具。
Clin Anat. 2017 Sep;30(6):736-741. doi: 10.1002/ca.22943. Epub 2017 Jul 6.
3
Adaptive Monocular Visual-Inertial SLAM for Real-Time Augmented Reality Applications in Mobile Devices.适用于移动设备实时增强现实应用的自适应单目视觉惯性同步定位与地图构建
Sensors (Basel). 2017 Nov 7;17(11):2567. doi: 10.3390/s17112567.
4
Real-time markerless tracking for augmented reality: the virtual visual servoing framework.用于增强现实的实时无标记跟踪：虚拟视觉伺服框架。
IEEE Trans Vis Comput Graph. 2006 Jul-Aug;12(4):615-28. doi: 10.1109/TVCG.2006.78.
5
Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones.用于手机上广域增强现实的传感器感知识别与跟踪
Sensors (Basel). 2015 Dec 10;15(12):31092-107. doi: 10.3390/s151229847.
6
A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices.一种用于融合低成本 IMU/磁力计输出的方法，用于移动设备上的应用。
Sensors (Basel). 2018 Aug 9;18(8):2616. doi: 10.3390/s18082616.
7
Efficient and Scalable Object Localization in 3D on Mobile Device.移动设备上3D场景中高效且可扩展的目标定位
J Imaging. 2022 Jul 8;8(7):188. doi: 10.3390/jimaging8070188.
8
Learning anatomy via mobile augmented reality: Effects on achievement and cognitive load.通过移动增强现实学习解剖学：对成绩和认知负荷的影响。
Anat Sci Educ. 2016 Oct;9(5):411-21. doi: 10.1002/ase.1603. Epub 2016 Mar 7.
9
Real-time occlusion handling in augmented reality based on an object tracking approach.基于目标跟踪方法的增强现实中的实时遮挡处理。
Sensors (Basel). 2010;10(4):2885-900. doi: 10.3390/s100402885. Epub 2010 Mar 29.
10
Registration using natural features for augmented reality systems.用于增强现实系统的基于自然特征的注册。
IEEE Trans Vis Comput Graph. 2006 Jul-Aug;12(4):569-80. doi: 10.1109/TVCG.2006.79.

引用本文的文献

1
An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web.基于增强现实和语义网的移动设备室内导航方法
Sensors (Basel). 2021 Aug 12;21(16):5435. doi: 10.3390/s21165435.
2
Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review.增强现实接口在中枢神经系统复杂解剖学学习中的应用：系统评价。
J Healthc Eng. 2020 Sep 10;2020:8835544. doi: 10.1155/2020/8835544. eCollection 2020.

本文引用的文献

1
Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.基于区域卷积神经网络和难负样本挖掘的航空图像车辆检测
Sensors (Basel). 2017 Feb 10;17(2):336. doi: 10.3390/s17020336.
2
A Real-Time Marker-Based Visual Sensor Based on a FPGA and a Soft Core Processor.一种基于FPGA和软核处理器的基于标记的实时视觉传感器。
Sensors (Basel). 2016 Dec 15;16(12):2139. doi: 10.3390/s16122139.
3
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.更快的 R-CNN：基于区域建议网络的实时目标检测。
IEEE Trans Pattern Anal Mach Intell. 2017 Jun;39(6):1137-1149. doi: 10.1109/TPAMI.2016.2577031. Epub 2016 Jun 6.
4
What Makes for Effective Detection Proposals?什么因素能促成有效的检测提议？
IEEE Trans Pattern Anal Mach Intell. 2016 Apr;38(4):814-30. doi: 10.1109/TPAMI.2015.2465908.
5
Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones.用于手机上广域增强现实的传感器感知识别与跟踪
Sensors (Basel). 2015 Dec 10;15(12):31092-107. doi: 10.3390/s151229847.
6
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.空间金字塔池化在深度卷积网络中的视觉识别。
IEEE Trans Pattern Anal Mach Intell. 2015 Sep;37(9):1904-16. doi: 10.1109/TPAMI.2015.2389824.
7
Deep learning.深度学习。
Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
8
Registration Combining Wide and Narrow Baseline Feature Tracking Techniques for Markerless AR Systems.基于广基线和窄基线特征跟踪技术的无标记增强现实系统注册方法。
Sensors (Basel). 2009;9(12):10097-116. doi: 10.3390/s91210097. Epub 2009 Dec 11.
9
Integrating geographical information and augmented reality techniques for mobile escape guidelines on nuclear accident sites.将地理信息与增强现实技术集成，为核事故现场的移动逃生指南提供支持。
J Environ Radioact. 2012 Jul;109:36-44. doi: 10.1016/j.jenvrad.2011.12.025. Epub 2012 Jan 17.
10
Fast scene recognition and camera relocalisation for wide area augmented reality systems.用于大范围增强现实系统的快速场景识别和相机重定位。
Sensors (Basel). 2010;10(6):6017-43. doi: 10.3390/s100606017. Epub 2010 Jun 14.