Jiachen Yang- IEEE Ind. Infor -2020.pdf (6.95 MB)
Download fileFADN: Fully connected attitude detection network based on industrial video
journal contribution
posted on 2020-09-18, 10:43 authored by Jiachen Yang, Meng Xi, Bin Jiang, Jiabao Man, Qinggang MengQinggang Meng, Baihua LiBaihua 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 InformaticsVolume
17Issue
3Pages
2011 - 2020Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher 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
2020-03-24Publication date
2020-04-02Copyright date
2020ISSN
1551-3203eISSN
1941-0050Publisher version
Language
- en