Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images

This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrar...

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Autores principales:	Xin Li, Yonggang Li, Renchao Wu, Can Zhou, Hongqiu Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	sample synthesis short circuit detection infrared image metal electrorefining attention-based Faster R-CNN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/95510d06f88a4b78b72ff842179557a0
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id	oai:doaj.org-article:95510d06f88a4b78b72ff842179557a0
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spelling	oai:doaj.org-article:95510d06f88a4b78b72ff842179557a02021-12-01T08:21:12ZShort Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images1662-521810.3389/fnbot.2021.751037https://doaj.org/article/95510d06f88a4b78b72ff842179557a02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnbot.2021.751037/fullhttps://doaj.org/toc/1662-5218This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrared images is proposed. After discussing factors that affect sample diversity, background, object shape, and gray scale distribution are established as three key variables for synthesis. Raw infrared images without fault are used as backgrounds. By simulating the other two key variables on the background, different classes of objects are synthesized. To improve the detection rate of small scale targets, an attention module is introduced in the network to fuse the semantic segment results of U-Net and the synthetic dataset. In this way, the Faster R-CNN can obtain rich representation ability about small scale object on the infrared images. Strategies of parameter tuning and transfer learning are also applied to improve the detection precision. The detection system trains on only synthetic dataset and tests on actual images. Extensive experiments on different infrared datasets demonstrate the effectiveness of the synthetic methods. The synthetically trained network obtains a mAP of 0.826, and the recall rate of small latent short circuit is superior to that of Faster R-CNN and U-Net, effectively avoiding short-circuit missed detection.Xin LiYonggang LiRenchao WuCan ZhouHongqiu ZhuFrontiers Media S.A.articlesample synthesisshort circuit detectioninfrared imagemetal electrorefiningattention-based Faster R-CNNNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neurorobotics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sample synthesis short circuit detection infrared image metal electrorefining attention-based Faster R-CNN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	sample synthesis short circuit detection infrared image metal electrorefining attention-based Faster R-CNN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Xin Li Yonggang Li Renchao Wu Can Zhou Hongqiu Zhu Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
description	This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrared images is proposed. After discussing factors that affect sample diversity, background, object shape, and gray scale distribution are established as three key variables for synthesis. Raw infrared images without fault are used as backgrounds. By simulating the other two key variables on the background, different classes of objects are synthesized. To improve the detection rate of small scale targets, an attention module is introduced in the network to fuse the semantic segment results of U-Net and the synthetic dataset. In this way, the Faster R-CNN can obtain rich representation ability about small scale object on the infrared images. Strategies of parameter tuning and transfer learning are also applied to improve the detection precision. The detection system trains on only synthetic dataset and tests on actual images. Extensive experiments on different infrared datasets demonstrate the effectiveness of the synthetic methods. The synthetically trained network obtains a mAP of 0.826, and the recall rate of small latent short circuit is superior to that of Faster R-CNN and U-Net, effectively avoiding short-circuit missed detection.
format	article
author	Xin Li Yonggang Li Renchao Wu Can Zhou Hongqiu Zhu
author_facet	Xin Li Yonggang Li Renchao Wu Can Zhou Hongqiu Zhu
author_sort	Xin Li
title	Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
title_short	Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
title_full	Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
title_fullStr	Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
title_full_unstemmed	Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images
title_sort	short circuit recognition for metal electrorefining using an improved faster r-cnn with synthetic infrared images
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/95510d06f88a4b78b72ff842179557a0
work_keys_str_mv	AT xinli shortcircuitrecognitionformetalelectrorefiningusinganimprovedfasterrcnnwithsyntheticinfraredimages AT yonggangli shortcircuitrecognitionformetalelectrorefiningusinganimprovedfasterrcnnwithsyntheticinfraredimages AT renchaowu shortcircuitrecognitionformetalelectrorefiningusinganimprovedfasterrcnnwithsyntheticinfraredimages AT canzhou shortcircuitrecognitionformetalelectrorefiningusinganimprovedfasterrcnnwithsyntheticinfraredimages AT hongqiuzhu shortcircuitrecognitionformetalelectrorefiningusinganimprovedfasterrcnnwithsyntheticinfraredimages
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Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images

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