Salient feature fusion convolutional network for multi-class meters detection

Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detect...
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Autor*in:	Wang, Zhaolin [verfasserIn] Tian, Lianfang Du, Qiliang Sun, Zhengzheng Liao, Wenzhi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Object detection Feature pyramid network Attention mechanism Meter detection Multi-class meters

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 18(2023), 2 vom: 31. Okt., Seite 1183-1192
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:2 ; day:31 ; month:10 ; pages:1183-1192

Links:	Volltext

DOI / URN:	10.1007/s11760-023-02721-w

Katalog-ID:	SPR054836115

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520			\|a Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters.
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allfields	10.1007/s11760-023-02721-w doi (DE-627)SPR054836115 (SPR)s11760-023-02721-w-e DE-627 ger DE-627 rakwb eng Wang, Zhaolin verfasserin aut Salient feature fusion convolutional network for multi-class meters detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. Object detection (dpeaa)DE-He213 Feature pyramid network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Meter detection (dpeaa)DE-He213 Multi-class meters (dpeaa)DE-He213 Tian, Lianfang aut Du, Qiliang aut Sun, Zhengzheng aut Liao, Wenzhi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 31. Okt., Seite 1183-1192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192 https://dx.doi.org/10.1007/s11760-023-02721-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 31 10 1183-1192
spelling	10.1007/s11760-023-02721-w doi (DE-627)SPR054836115 (SPR)s11760-023-02721-w-e DE-627 ger DE-627 rakwb eng Wang, Zhaolin verfasserin aut Salient feature fusion convolutional network for multi-class meters detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. Object detection (dpeaa)DE-He213 Feature pyramid network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Meter detection (dpeaa)DE-He213 Multi-class meters (dpeaa)DE-He213 Tian, Lianfang aut Du, Qiliang aut Sun, Zhengzheng aut Liao, Wenzhi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 31. Okt., Seite 1183-1192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192 https://dx.doi.org/10.1007/s11760-023-02721-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 31 10 1183-1192
allfields_unstemmed	10.1007/s11760-023-02721-w doi (DE-627)SPR054836115 (SPR)s11760-023-02721-w-e DE-627 ger DE-627 rakwb eng Wang, Zhaolin verfasserin aut Salient feature fusion convolutional network for multi-class meters detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. Object detection (dpeaa)DE-He213 Feature pyramid network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Meter detection (dpeaa)DE-He213 Multi-class meters (dpeaa)DE-He213 Tian, Lianfang aut Du, Qiliang aut Sun, Zhengzheng aut Liao, Wenzhi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 31. Okt., Seite 1183-1192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192 https://dx.doi.org/10.1007/s11760-023-02721-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 31 10 1183-1192
allfieldsGer	10.1007/s11760-023-02721-w doi (DE-627)SPR054836115 (SPR)s11760-023-02721-w-e DE-627 ger DE-627 rakwb eng Wang, Zhaolin verfasserin aut Salient feature fusion convolutional network for multi-class meters detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. Object detection (dpeaa)DE-He213 Feature pyramid network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Meter detection (dpeaa)DE-He213 Multi-class meters (dpeaa)DE-He213 Tian, Lianfang aut Du, Qiliang aut Sun, Zhengzheng aut Liao, Wenzhi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 31. Okt., Seite 1183-1192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192 https://dx.doi.org/10.1007/s11760-023-02721-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 31 10 1183-1192
allfieldsSound	10.1007/s11760-023-02721-w doi (DE-627)SPR054836115 (SPR)s11760-023-02721-w-e DE-627 ger DE-627 rakwb eng Wang, Zhaolin verfasserin aut Salient feature fusion convolutional network for multi-class meters detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. Object detection (dpeaa)DE-He213 Feature pyramid network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Meter detection (dpeaa)DE-He213 Multi-class meters (dpeaa)DE-He213 Tian, Lianfang aut Du, Qiliang aut Sun, Zhengzheng aut Liao, Wenzhi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 31. Okt., Seite 1183-1192 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192 https://dx.doi.org/10.1007/s11760-023-02721-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 31 10 1183-1192
language	English
source	Enthalten in Signal, image and video processing 18(2023), 2 vom: 31. Okt., Seite 1183-1192 volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192
sourceStr	Enthalten in Signal, image and video processing 18(2023), 2 vom: 31. Okt., Seite 1183-1192 volume:18 year:2023 number:2 day:31 month:10 pages:1183-1192
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Object detection Feature pyramid network Attention mechanism Meter detection Multi-class meters
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Wang, Zhaolin @@aut@@ Tian, Lianfang @@aut@@ Du, Qiliang @@aut@@ Sun, Zhengzheng @@aut@@ Liao, Wenzhi @@aut@@
publishDateDaySort_date	2023-10-31T00:00:00Z
hierarchy_top_id	546899102
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author	Wang, Zhaolin
spellingShingle	Wang, Zhaolin misc Object detection misc Feature pyramid network misc Attention mechanism misc Meter detection misc Multi-class meters Salient feature fusion convolutional network for multi-class meters detection
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topic_title	Salient feature fusion convolutional network for multi-class meters detection Object detection (dpeaa)DE-He213 Feature pyramid network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Meter detection (dpeaa)DE-He213 Multi-class meters (dpeaa)DE-He213
topic	misc Object detection misc Feature pyramid network misc Attention mechanism misc Meter detection misc Multi-class meters
topic_unstemmed	misc Object detection misc Feature pyramid network misc Attention mechanism misc Meter detection misc Multi-class meters
topic_browse	misc Object detection misc Feature pyramid network misc Attention mechanism misc Meter detection misc Multi-class meters
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title	Salient feature fusion convolutional network for multi-class meters detection
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title_full	Salient feature fusion convolutional network for multi-class meters detection
author_sort	Wang, Zhaolin
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author_browse	Wang, Zhaolin Tian, Lianfang Du, Qiliang Sun, Zhengzheng Liao, Wenzhi
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doi_str_mv	10.1007/s11760-023-02721-w
title_sort	salient feature fusion convolutional network for multi-class meters detection
title_auth	Salient feature fusion convolutional network for multi-class meters detection
abstract	Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Salient feature fusion convolutional network for multi-class meters detection
url	https://dx.doi.org/10.1007/s11760-023-02721-w
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author2	Tian, Lianfang Du, Qiliang Sun, Zhengzheng Liao, Wenzhi
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up_date	2024-07-04T03:12:19.295Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054836115</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240221064701.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240221s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11760-023-02721-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054836115</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11760-023-02721-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Zhaolin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Salient feature fusion convolutional network for multi-class meters detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model classification is a difficult task because of the great similarity in appearance of the different meters. Existing methods mostly focus on detecting and reading individual types of meters, while neglecting to put the classification of multi-class meters on the map. For that, we propose a salient feature fusion convolutional network (SFFCN) for meter model classification and detecting multi-class meters, both of which are crucial steps for further meter readings. The central fusion pyramid network within SFFCN is an improved FPN that efficiently extracts and fuses multi-scale features, thereby enhancing feature saliency and diversity. To improve the accuracy of localization and classification, we introduce a classification-weighted localization attention module (CLAM) to the detection heads. CLAM guides the location branch based on the feature map of the classification branch, mitigating the mismatch between classification confidence and localization accuracy. The experimental results demonstrate that the proposed SFFCN achieves 88.8 mAP on the power meter image dataset and reaches a speed of 68.1 FPS on RTX 3080Ti, effectively improving the detection and classification of multi-class meters.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Object detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature pyramid network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Meter detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-class meters</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Lianfang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Qiliang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Zhengzheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liao, Wenzhi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Signal, image and video processing</subfield><subfield code="d">London [u.a.] : Springer, 2007</subfield><subfield code="g">18(2023), 2 vom: 31. Okt., Seite 1183-1192</subfield><subfield code="w">(DE-627)546899102</subfield><subfield code="w">(DE-600)2391619-9</subfield><subfield code="x">1863-1711</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:18</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2</subfield><subfield code="g">day:31</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:1183-1192</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11760-023-02721-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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