Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion

As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging t...
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Gespeichert in:

Autor*in:	Li, Liangliang [verfasserIn] Ren, Jia [verfasserIn] Wang, Peng [verfasserIn] Lü, Zhigang [verfasserIn] Di, RuoHai [verfasserIn] Li, Xiaoyan [verfasserIn] Gao, Hui [verfasserIn] Zhao, Xiangmo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Weld defect detection High resolution Feature reorganization Multi-scale feature fusion

Übergeordnetes Werk:	Enthalten in: Mechanical systems and signal processing - Amsterdam [u.a.] : Elsevier, 1987, 199
Übergeordnetes Werk:	volume:199

DOI / URN:	10.1016/j.ymssp.2023.110484

Katalog-ID:	ELV010531114

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245	1	0	\|a Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion
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520			\|a As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection.
650		4	\|a Weld defect detection
650		4	\|a High resolution
650		4	\|a Feature reorganization
650		4	\|a Multi-scale feature fusion
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700	1		\|a Wang, Peng \|e verfasserin \|4 aut
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700	1		\|a Di, RuoHai \|e verfasserin \|4 aut
700	1		\|a Li, Xiaoyan \|e verfasserin \|4 aut
700	1		\|a Gao, Hui \|e verfasserin \|4 aut
700	1		\|a Zhao, Xiangmo \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Mechanical systems and signal processing \|d Amsterdam [u.a.] : Elsevier, 1987 \|g 199 \|h Online-Ressource \|w (DE-627)267838670 \|w (DE-600)1471003-1 \|w (DE-576)253127629 \|x 1096-1216 \|7 nnns
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publishDate	2023
allfields	10.1016/j.ymssp.2023.110484 doi (DE-627)ELV010531114 (ELSEVIER)S0888-3270(23)00392-8 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Li, Liangliang verfasserin aut Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection. Weld defect detection High resolution Feature reorganization Multi-scale feature fusion Ren, Jia verfasserin aut Wang, Peng verfasserin aut Lü, Zhigang verfasserin aut Di, RuoHai verfasserin aut Li, Xiaoyan verfasserin aut Gao, Hui verfasserin aut Zhao, Xiangmo verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 199 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:199 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 199
spelling	10.1016/j.ymssp.2023.110484 doi (DE-627)ELV010531114 (ELSEVIER)S0888-3270(23)00392-8 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Li, Liangliang verfasserin aut Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection. Weld defect detection High resolution Feature reorganization Multi-scale feature fusion Ren, Jia verfasserin aut Wang, Peng verfasserin aut Lü, Zhigang verfasserin aut Di, RuoHai verfasserin aut Li, Xiaoyan verfasserin aut Gao, Hui verfasserin aut Zhao, Xiangmo verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 199 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:199 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 199
allfields_unstemmed	10.1016/j.ymssp.2023.110484 doi (DE-627)ELV010531114 (ELSEVIER)S0888-3270(23)00392-8 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Li, Liangliang verfasserin aut Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection. Weld defect detection High resolution Feature reorganization Multi-scale feature fusion Ren, Jia verfasserin aut Wang, Peng verfasserin aut Lü, Zhigang verfasserin aut Di, RuoHai verfasserin aut Li, Xiaoyan verfasserin aut Gao, Hui verfasserin aut Zhao, Xiangmo verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 199 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:199 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 199
allfieldsGer	10.1016/j.ymssp.2023.110484 doi (DE-627)ELV010531114 (ELSEVIER)S0888-3270(23)00392-8 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Li, Liangliang verfasserin aut Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection. Weld defect detection High resolution Feature reorganization Multi-scale feature fusion Ren, Jia verfasserin aut Wang, Peng verfasserin aut Lü, Zhigang verfasserin aut Di, RuoHai verfasserin aut Li, Xiaoyan verfasserin aut Gao, Hui verfasserin aut Zhao, Xiangmo verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 199 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:199 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 199
allfieldsSound	10.1016/j.ymssp.2023.110484 doi (DE-627)ELV010531114 (ELSEVIER)S0888-3270(23)00392-8 DE-627 ger DE-627 rda eng 004 VZ 50.32 bkl 50.16 bkl Li, Liangliang verfasserin aut Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection. Weld defect detection High resolution Feature reorganization Multi-scale feature fusion Ren, Jia verfasserin aut Wang, Peng verfasserin aut Lü, Zhigang verfasserin aut Di, RuoHai verfasserin aut Li, Xiaoyan verfasserin aut Gao, Hui verfasserin aut Zhao, Xiangmo verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 199 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:199 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.16 Technische Zuverlässigkeit Instandhaltung VZ AR 199
language	English
source	Enthalten in Mechanical systems and signal processing 199 volume:199
sourceStr	Enthalten in Mechanical systems and signal processing 199 volume:199
format_phy_str_mv	Article
bklname	Dynamik Schwingungslehre Technische Zuverlässigkeit Instandhaltung
institution	findex.gbv.de
topic_facet	Weld defect detection High resolution Feature reorganization Multi-scale feature fusion
dewey-raw	004
isfreeaccess_bool	false
container_title	Mechanical systems and signal processing
authorswithroles_txt_mv	Li, Liangliang @@aut@@ Ren, Jia @@aut@@ Wang, Peng @@aut@@ Lü, Zhigang @@aut@@ Di, RuoHai @@aut@@ Li, Xiaoyan @@aut@@ Gao, Hui @@aut@@ Zhao, Xiangmo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	267838670
dewey-sort	14
id	ELV010531114
language_de	englisch
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author	Li, Liangliang
spellingShingle	Li, Liangliang ddc 004 bkl 50.32 bkl 50.16 misc Weld defect detection misc High resolution misc Feature reorganization misc Multi-scale feature fusion Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion
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topic_title	004 VZ 50.32 bkl 50.16 bkl Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion Weld defect detection High resolution Feature reorganization Multi-scale feature fusion
topic	ddc 004 bkl 50.32 bkl 50.16 misc Weld defect detection misc High resolution misc Feature reorganization misc Multi-scale feature fusion
topic_unstemmed	ddc 004 bkl 50.32 bkl 50.16 misc Weld defect detection misc High resolution misc Feature reorganization misc Multi-scale feature fusion
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title	Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion
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title_full	Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion
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author_browse	Li, Liangliang Ren, Jia Wang, Peng Lü, Zhigang Di, RuoHai Li, Xiaoyan Gao, Hui Zhao, Xiangmo
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title_sort	defect detection method for high-resolution weld based on wandering gaussian and multi-feature enhancement fusion
title_auth	Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion
abstract	As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection.
abstractGer	As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection.
abstract_unstemmed	As the weakest point of the pipeline, the weld seam is prone to various internal defects and has great safety risks, so it is necessary to conduct a weld seam inspection. In addition, accurately detecting small-sized defects from high-resolution and low-contrast X-ray images is still a challenging task. Therefore, this paper proposes a defect detection method for high-resolution weld images, including three steps: welded extraction, weld restructuring, and defect detection. Because the resolution of the original image is high and the defect features are mainly concentrated in the weld area, it is not easy to directly defect detection. Firstly, a new irregular long weld extraction algorithm based on wandering Gaussian was designed. Secondly, because the existing welding seams aspect ratio is large and the information loss of conventional compression methods is serious, a new feature reorganization method was provided to maximize the effective information of the weld. Finally, a new method of welding defects detection method based on cross-layer feature fusion has been redesigned to take into efficiency and accuracy. The experiments show that the proposed method achieves the best detection performance on the public GDXray dataset compared with other advanced defect detection methods, from which it can locate the weld defects automatically and fast with high accuracy. It provides an effective solution for high-resolution weld defect detection.
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title_short	Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion
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author2	Ren, Jia Wang, Peng Lü, Zhigang Di, RuoHai Li, Xiaoyan Gao, Hui Zhao, Xiangmo
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up_date	2024-07-06T18:19:07.617Z
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Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion

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