Target State Classification by Attention-Based Branch Expansion Network

The intelligent laboratory is an important carrier for the development of the manufacturing industry. In order to meet the technical state requirements of the laboratory and control the particle redundancy, the wearing state of personnel and the technical state of objects are very important observat...

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Autores principales:	Yue Zhang, Shengli Sun, Huikai Liu, Linjian Lei, Gaorui Liu, Dehui Lu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	technical state requirements target state classification branch expansion dual-attention module parallel residual connection stacked block Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3098baac85724df995c5b4e67fc49c6a
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id	oai:doaj.org-article:3098baac85724df995c5b4e67fc49c6a
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spelling	oai:doaj.org-article:3098baac85724df995c5b4e67fc49c6a2021-11-11T15:15:42ZTarget State Classification by Attention-Based Branch Expansion Network10.3390/app1121102082076-3417https://doaj.org/article/3098baac85724df995c5b4e67fc49c6a2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10208https://doaj.org/toc/2076-3417The intelligent laboratory is an important carrier for the development of the manufacturing industry. In order to meet the technical state requirements of the laboratory and control the particle redundancy, the wearing state of personnel and the technical state of objects are very important observation indicators in the digital laboratory. We collect human and object state datasets, which present the state classification challenge of the staff and experimental tools. Humans and objects are especially important for scene understanding, especially those existing in scenarios that have an impact on the current task. Based on the characteristics of the above datasets—small inter-class distance and large intra-class distance—an attention-based branch expansion network (ABE) is proposed to distinguish confounding features. In order to achieve the best recognition effect by considering the network’s depth and width, we firstly carry out a multi-dimensional reorganization of the existing network structure to explore the influence of depth and width on feature expression by comparing four networks with different depths and widths. We apply channel and spatial attention to refine the features extracted by the four networks, which learn “what” and “where”, respectively, to focus. We find the best results lie in the parallel residual connection of the dual attention applied in stacked block mode. We conduct extensive ablation analysis, gain consistent improvements in classification performance on various datasets, demonstrate the effectiveness of the dual-attention-based branch expansion network, and show a wide range of applicability. It achieves comparable performance with the state of the art (SOTA) on the common dataset Trashnet, with an accuracy of 94.53%.Yue ZhangShengli SunHuikai LiuLinjian LeiGaorui LiuDehui LuMDPI AGarticletechnical state requirementstarget state classificationbranch expansiondual-attention moduleparallel residual connectionstacked blockTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10208, p 10208 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	technical state requirements target state classification branch expansion dual-attention module parallel residual connection stacked block Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	technical state requirements target state classification branch expansion dual-attention module parallel residual connection stacked block Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yue Zhang Shengli Sun Huikai Liu Linjian Lei Gaorui Liu Dehui Lu Target State Classification by Attention-Based Branch Expansion Network
description	The intelligent laboratory is an important carrier for the development of the manufacturing industry. In order to meet the technical state requirements of the laboratory and control the particle redundancy, the wearing state of personnel and the technical state of objects are very important observation indicators in the digital laboratory. We collect human and object state datasets, which present the state classification challenge of the staff and experimental tools. Humans and objects are especially important for scene understanding, especially those existing in scenarios that have an impact on the current task. Based on the characteristics of the above datasets—small inter-class distance and large intra-class distance—an attention-based branch expansion network (ABE) is proposed to distinguish confounding features. In order to achieve the best recognition effect by considering the network’s depth and width, we firstly carry out a multi-dimensional reorganization of the existing network structure to explore the influence of depth and width on feature expression by comparing four networks with different depths and widths. We apply channel and spatial attention to refine the features extracted by the four networks, which learn “what” and “where”, respectively, to focus. We find the best results lie in the parallel residual connection of the dual attention applied in stacked block mode. We conduct extensive ablation analysis, gain consistent improvements in classification performance on various datasets, demonstrate the effectiveness of the dual-attention-based branch expansion network, and show a wide range of applicability. It achieves comparable performance with the state of the art (SOTA) on the common dataset Trashnet, with an accuracy of 94.53%.
format	article
author	Yue Zhang Shengli Sun Huikai Liu Linjian Lei Gaorui Liu Dehui Lu
author_facet	Yue Zhang Shengli Sun Huikai Liu Linjian Lei Gaorui Liu Dehui Lu
author_sort	Yue Zhang
title	Target State Classification by Attention-Based Branch Expansion Network
title_short	Target State Classification by Attention-Based Branch Expansion Network
title_full	Target State Classification by Attention-Based Branch Expansion Network
title_fullStr	Target State Classification by Attention-Based Branch Expansion Network
title_full_unstemmed	Target State Classification by Attention-Based Branch Expansion Network
title_sort	target state classification by attention-based branch expansion network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3098baac85724df995c5b4e67fc49c6a
work_keys_str_mv	AT yuezhang targetstateclassificationbyattentionbasedbranchexpansionnetwork AT shenglisun targetstateclassificationbyattentionbasedbranchexpansionnetwork AT huikailiu targetstateclassificationbyattentionbasedbranchexpansionnetwork AT linjianlei targetstateclassificationbyattentionbasedbranchexpansionnetwork AT gaoruiliu targetstateclassificationbyattentionbasedbranchexpansionnetwork AT dehuilu targetstateclassificationbyattentionbasedbranchexpansionnetwork
_version_	1718435968088801280

Target State Classification by Attention-Based Branch Expansion Network

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