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RPN-FCN based rust detection on power equipment |
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| Author: | Yuan, Jiangru1; Xue, Bing2; Zhang, Weishan2; |
| Organizations: |
1Research Institute of Petroleum Exploration & development, CNPC, China 2Department of Software Engineering,China University of Petroleum, Qingdao, 266580, China 3Beijing University of Science and Technology, Beijing, China
4Qingdao Deep Intelligence Information Technology Co. LTD, Qingdao, China
5University of Oulu, Oulu, Finland |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 0.4 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2019101032129 |
| Language: | English |
| Published: |
Elsevier,
2019
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| Publish Date: | 2019-10-10 |
| Description: |
AbstractThis paper proposes a novel RPN-FCN based rust detection approach. The RPN-FCN generates region proposals with RPN and performs full convolution for semantic segmentation of rust. The experimental result demonstrate that this approach improves the accuracy of rust detection compared with other neural networks. see all
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| Series: |
Procedia. Computer science |
| ISSN: | 1877-0509 |
| ISSN-E: | 1877-0509 |
| ISSN-L: | 1877-0509 |
| Volume: | 147 |
| Pages: | 349 - 353 |
| DOI: | 10.1016/j.procs.2019.01.236 |
| OADOI: | https://oadoi.org/10.1016/j.procs.2019.01.236 |
| Host publication: |
2018 International Conference on Identification, Information and Knowledge in the Internet of Things |
| Host publication editor: |
Bie, Rongfang Sun, Yunchuan Yu, Jiguo |
| Conference: |
International Conference on Identification, Information and Knowledge in the Internet of Things |
| Type of Publication: |
A4 Article in conference proceedings |
| Field of Science: |
213 Electronic, automation and communications engineering, electronics 113 Computer and information sciences |
| Subjects: | |
| Funding: |
This research was supported in part by the National Major Science and Technology Project (2017ZX05013-002), the Key Research Program of Shandong Province (2017GGX10140) and in part by the Fundamental Research Fundsfor the Central Universities (2015020031), National Natural Science Foundation of China (61309024).
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| Copyright information: |
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
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https://creativecommons.org/licenses/by-nc-nd/4.0/ |