University of Oulu

Jiangru Yuan, Bing Xue, Weishan Zhang, Liang Xu, Haoyun Sun, Jiehan Zhou, RPN-FCN based Rust detection on power equipment, Procedia Computer Science, Volume 147, 2019, Pages 349-353, ISSN 1877-0509,

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)
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Language: English
Published: Elsevier, 2019
Publish Date: 2019-10-10


This 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.

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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
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
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).
Copyright information: © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (