Jiachen Yang- IEEE Ind. Infor -2020.pdf (6.95 MB)
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FADN: Fully connected attitude detection network based on industrial video

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journal contribution
posted on 18.09.2020, 10:43 authored by Jiachen Yang, Meng Xi, Bin Jiang, Jiabao Man, Qinggang MengQinggang Meng, Baihua Li
—In 3D attitude angle estimation, monocular visionbased methods are often utilized for the advantages of short-time and high efficiency. However, the limitations of these methods lie in the complexity of the algorithm and the specificity of the scene, which needs to match the characteristics of the cooperation object and the scene. We propose a fully connected attitude detection network (FADN) which combines neural network and traditional algorithms for 3D attitude angle estimation. FADN provides a whole process from the input of a single frame image in the industrial video stream to the output of the corresponding 3D attitude angle estimation. Benefiting from the end-to-end estimation framework, FADN avoids tedious matching algorithms and thus has certain portability. A series of comparative experiments based on the rendering software 3D Studio Max (3d Max) have been carried out to evaluate the performance of FADN. The experimental results show that FADN has high estimation accuracy and fast running speed. At the same time, the simulation results reliably prove the feasibility of FADN, and also promote the research in real scenarios.

Funding

National Natural Science Foundation of China (No. 61871283)

Foundation of Pre-Research on Equipment of China (NO.61400010304)

Major Civil-Military Integration Project in Tianjin, China (NO.18ZXJMTG00170)

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Industrial Informatics

Volume

17

Issue

3

Pages

2011 - 2020

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

24/03/2020

Publication date

2020-04-02

Copyright date

2020

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Dr Baihua Li Deposit date: 17 September 2020