A novel efficient method for welding spots detection
Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called M...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
He, Zhicheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 18 vom: 26. März, Seite 26381-26401 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:18 ; day:26 ; month:03 ; pages:26381-26401 |
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| DOI / URN: |
10.1007/s11042-022-12921-0 |
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SPR047470976 |
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| 520 | |a Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. | ||
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10.1007/s11042-022-12921-0 doi (DE-627)SPR047470976 (SPR)s11042-022-12921-0-e DE-627 ger DE-627 rakwb eng He, Zhicheng verfasserin aut A novel efficient method for welding spots detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. Welding spots detection (dpeaa)DE-He213 Light-weight deep learning model (dpeaa)DE-He213 Circle detection (dpeaa)DE-He213 Right-triangle sampling strategy (dpeaa)DE-He213 Ma, Yadong aut Wang, Zhenxing aut Li, Eric aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 18 vom: 26. März, Seite 26381-26401 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:18 day:26 month:03 pages:26381-26401 https://dx.doi.org/10.1007/s11042-022-12921-0 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_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_2119 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 81 2022 18 26 03 26381-26401 |
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10.1007/s11042-022-12921-0 doi (DE-627)SPR047470976 (SPR)s11042-022-12921-0-e DE-627 ger DE-627 rakwb eng He, Zhicheng verfasserin aut A novel efficient method for welding spots detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. Welding spots detection (dpeaa)DE-He213 Light-weight deep learning model (dpeaa)DE-He213 Circle detection (dpeaa)DE-He213 Right-triangle sampling strategy (dpeaa)DE-He213 Ma, Yadong aut Wang, Zhenxing aut Li, Eric aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 18 vom: 26. März, Seite 26381-26401 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:18 day:26 month:03 pages:26381-26401 https://dx.doi.org/10.1007/s11042-022-12921-0 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_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_2119 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 81 2022 18 26 03 26381-26401 |
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10.1007/s11042-022-12921-0 doi (DE-627)SPR047470976 (SPR)s11042-022-12921-0-e DE-627 ger DE-627 rakwb eng He, Zhicheng verfasserin aut A novel efficient method for welding spots detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. Welding spots detection (dpeaa)DE-He213 Light-weight deep learning model (dpeaa)DE-He213 Circle detection (dpeaa)DE-He213 Right-triangle sampling strategy (dpeaa)DE-He213 Ma, Yadong aut Wang, Zhenxing aut Li, Eric aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 18 vom: 26. März, Seite 26381-26401 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:18 day:26 month:03 pages:26381-26401 https://dx.doi.org/10.1007/s11042-022-12921-0 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_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_2119 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 81 2022 18 26 03 26381-26401 |
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10.1007/s11042-022-12921-0 doi (DE-627)SPR047470976 (SPR)s11042-022-12921-0-e DE-627 ger DE-627 rakwb eng He, Zhicheng verfasserin aut A novel efficient method for welding spots detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. Welding spots detection (dpeaa)DE-He213 Light-weight deep learning model (dpeaa)DE-He213 Circle detection (dpeaa)DE-He213 Right-triangle sampling strategy (dpeaa)DE-He213 Ma, Yadong aut Wang, Zhenxing aut Li, Eric aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 18 vom: 26. März, Seite 26381-26401 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:18 day:26 month:03 pages:26381-26401 https://dx.doi.org/10.1007/s11042-022-12921-0 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_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_2119 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 81 2022 18 26 03 26381-26401 |
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10.1007/s11042-022-12921-0 doi (DE-627)SPR047470976 (SPR)s11042-022-12921-0-e DE-627 ger DE-627 rakwb eng He, Zhicheng verfasserin aut A novel efficient method for welding spots detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. Welding spots detection (dpeaa)DE-He213 Light-weight deep learning model (dpeaa)DE-He213 Circle detection (dpeaa)DE-He213 Right-triangle sampling strategy (dpeaa)DE-He213 Ma, Yadong aut Wang, Zhenxing aut Li, Eric aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 18 vom: 26. März, Seite 26381-26401 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:18 day:26 month:03 pages:26381-26401 https://dx.doi.org/10.1007/s11042-022-12921-0 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_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_2119 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 81 2022 18 26 03 26381-26401 |
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novel efficient method for welding spots detection |
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A novel efficient method for welding spots detection |
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Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| title_short |
A novel efficient method for welding spots detection |
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https://dx.doi.org/10.1007/s11042-022-12921-0 |
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Ma, Yadong Wang, Zhenxing Li, Eric |
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Ma, Yadong Wang, Zhenxing Li, Eric |
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10.1007/s11042-022-12921-0 |
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2024-07-04T03:14:09.672Z |
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| score |
7.4013853 |