.png){kind=icon}
返回
An efficient foreign objects detection network for power substation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2021.104159.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A power substation is susceptible to intrusions of foreign objects. The intrusions can likely result in failures of power supplies. Therefore, recognizing foreign objects becomes important to ensure constant and stable power supplies. However, existing object recognition methods fail to achieve acceptable accuracy and perfor-mance. In this paper, we propose an efficient Foreign Objects Detection Network for Power Substation (FODN4PS) to improve the recognition accuracy with less time. FODN4PS consists of a Moving Object Region Ex-traction Network (MORE Net) and a classification network, where the MORE Net can get the position of foreign objects, and the classification network can recognize the category of foreign objects. Experimental results show that FODN4PS is faster and more accurate in object recognition than the Fast R-CNN and Mask R-CNN. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Power substation
Deep learning
Foreign objects detection
FODN4PS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容