Cascaded feature enhancement network model for real-time video monitoring of power system

The application of real-time monitoring has been widely used to detect the safety and stability of the electric power system. Traditional monitoring relies heavily on human judgment and is impossible to detect status in real-time. Recently, with the development of deep learning, the object detection...

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Autores principales:	Xitian Long, Zhe Zheng, Rui Liu, Wenpeng Cui, Yingying Chi, Haifeng Zhang, Yidong Yuan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Object detection Neural network Cascaded features Power system Deep learning for fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2fa9158403994dc4add590e90c01f5f4
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id	oai:doaj.org-article:2fa9158403994dc4add590e90c01f5f4
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spelling	oai:doaj.org-article:2fa9158403994dc4add590e90c01f5f42021-11-28T04:33:41ZCascaded feature enhancement network model for real-time video monitoring of power system2352-484710.1016/j.egyr.2021.05.046https://doaj.org/article/2fa9158403994dc4add590e90c01f5f42021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721003255https://doaj.org/toc/2352-4847The application of real-time monitoring has been widely used to detect the safety and stability of the electric power system. Traditional monitoring relies heavily on human judgment and is impossible to detect status in real-time. Recently, with the development of deep learning, the object detection algorithm based on the deep convolutional neural network becomes a great option for realizing real-time monitoring applications of the power system. However, in power system scenarios, failed or unreal-time detection of abnormal conditions may cause a hazardous accident. To apply and optimize the object detection algorithm, issues such as multi-scale objects, class imbalance, and difficulty in balance speed and accuracy need to be addressed to improve the detection performance. Thus, we present a cascaded feature enhancement network model that combining attention mechanism, feature fusion scheme, and Cascaded Refinement Scheme. Attention mechanism and feature fusion scheme can help extract more effective feature information of multi-scale objects. Cascaded Refinement Scheme can effectively solve the problem of class imbalance. The whole model can well balanced in detect speed and accuracy. Experiments are performed on two benchmarks: PSA_Datasets and PASCAL VOC. Our method gets an absolute gain of 1.6% (300×300 input), 2.6% (512×512 input) in terms of mAP result of PSA_Datasets and 1% (300×300 input), 1.6% (512×512 input) in PASCAL VOC Dataset, compared to the best results of other SOTA detectors.Xitian LongZhe ZhengRui LiuWenpeng CuiYingying ChiHaifeng ZhangYidong YuanElsevierarticleObject detectionNeural networkCascaded featuresPower systemDeep learning for fault diagnosisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8485-8492 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Object detection Neural network Cascaded features Power system Deep learning for fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Object detection Neural network Cascaded features Power system Deep learning for fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xitian Long Zhe Zheng Rui Liu Wenpeng Cui Yingying Chi Haifeng Zhang Yidong Yuan Cascaded feature enhancement network model for real-time video monitoring of power system
description	The application of real-time monitoring has been widely used to detect the safety and stability of the electric power system. Traditional monitoring relies heavily on human judgment and is impossible to detect status in real-time. Recently, with the development of deep learning, the object detection algorithm based on the deep convolutional neural network becomes a great option for realizing real-time monitoring applications of the power system. However, in power system scenarios, failed or unreal-time detection of abnormal conditions may cause a hazardous accident. To apply and optimize the object detection algorithm, issues such as multi-scale objects, class imbalance, and difficulty in balance speed and accuracy need to be addressed to improve the detection performance. Thus, we present a cascaded feature enhancement network model that combining attention mechanism, feature fusion scheme, and Cascaded Refinement Scheme. Attention mechanism and feature fusion scheme can help extract more effective feature information of multi-scale objects. Cascaded Refinement Scheme can effectively solve the problem of class imbalance. The whole model can well balanced in detect speed and accuracy. Experiments are performed on two benchmarks: PSA_Datasets and PASCAL VOC. Our method gets an absolute gain of 1.6% (300×300 input), 2.6% (512×512 input) in terms of mAP result of PSA_Datasets and 1% (300×300 input), 1.6% (512×512 input) in PASCAL VOC Dataset, compared to the best results of other SOTA detectors.
format	article
author	Xitian Long Zhe Zheng Rui Liu Wenpeng Cui Yingying Chi Haifeng Zhang Yidong Yuan
author_facet	Xitian Long Zhe Zheng Rui Liu Wenpeng Cui Yingying Chi Haifeng Zhang Yidong Yuan
author_sort	Xitian Long
title	Cascaded feature enhancement network model for real-time video monitoring of power system
title_short	Cascaded feature enhancement network model for real-time video monitoring of power system
title_full	Cascaded feature enhancement network model for real-time video monitoring of power system
title_fullStr	Cascaded feature enhancement network model for real-time video monitoring of power system
title_full_unstemmed	Cascaded feature enhancement network model for real-time video monitoring of power system
title_sort	cascaded feature enhancement network model for real-time video monitoring of power system
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2fa9158403994dc4add590e90c01f5f4
work_keys_str_mv	AT xitianlong cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem AT zhezheng cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem AT ruiliu cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem AT wenpengcui cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem AT yingyingchi cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem AT haifengzhang cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem AT yidongyuan cascadedfeatureenhancementnetworkmodelforrealtimevideomonitoringofpowersystem
_version_	1718408308446986240

Cascaded feature enhancement network model for real-time video monitoring of power system

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