Weishan Zhang, Xia Liu, Jiangru Yuan, Liang Xu, Haoyun Sun, Jiehan Zhou, Xin Liu, RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL), Procedia Computer Science, Volume 147, 2019, Pages 331-337, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.01.232
RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL)
|Author:||Zhang, Weishan1; Liu, Xia1; Yuan, Jiangru2;|
1College of computer and Communication Engineering, China University of Petroleum,Qingdao, China
2Research Institute of Petroleum Exploration & development, CNPC, China
3College of computer and Communication Engineering, Beijing University of Science and Technology, Beijing, China
4Qingdao Deep Intelligence Information Technology Co. LTD, Qingdao, China
5University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019101032146
|Publish Date:|| 2019-10-10
This paper proposes a new deep learning network — RCNN4SPTL (RCNN -based Foreign Object Detection for Securing Power Transmission lines), which is suitable for detecting foreign objects on power transmission lines. The RCNN4SPTL uses RPN (Region Proposal Network) to generate aspect ratio of the region proposals to align with the size of foreign objects. The RCNN4SPTL uses an end to end training to improve its performance. Experimental results show that the RCNN4SPTL significantly improves the detection speed and recognition accuracy, compared with the original Faster RCNN.
Procedia. Computer science
|Pages:||331 - 337|
|Host publication editor:||
2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
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).
© 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/).