A rigorous fastener inspection approach for high-speed railway from structured light sensors

Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous h...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mao, Qingzhou [verfasserIn] Cui, Hao [verfasserIn] Hu, Qingwu [verfasserIn] Ren, Xiaochun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction

Übergeordnetes Werk:	Enthalten in: ISPRS journal of photogrammetry and remote sensing - International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7, Amsterdam [u.a.] : Elsevier, 1989, 143, Seite 249-267
Übergeordnetes Werk:	volume:143 ; pages:249-267

DOI / URN:	10.1016/j.isprsjprs.2017.11.007

Katalog-ID:	ELV00022622X

Internformat


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245	1	0	\|a A rigorous fastener inspection approach for high-speed railway from structured light sensors
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520			\|a Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
650		4	\|a High-speed railway
650		4	\|a Fastener inspection
650		4	\|a Structured light sensor
650		4	\|a Dense point cloud
650		4	\|a Decision tree
650		4	\|a Centerline extraction
700	1		\|a Cui, Hao \|e verfasserin \|0 (orcid)0000-0002-5121-3669 \|4 aut
700	1		\|a Hu, Qingwu \|e verfasserin \|0 (orcid)0000-0003-0866-6678 \|4 aut
700	1		\|a Ren, Xiaochun \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|a International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 \|t ISPRS journal of photogrammetry and remote sensing \|d Amsterdam [u.a.] : Elsevier, 1989 \|g 143, Seite 249-267 \|h Online-Ressource \|w (DE-627)320504557 \|w (DE-600)2012663-3 \|w (DE-576)096806567 \|x 0924-2716 \|7 nnns
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Indexfelder

author_variant	q m qm h c hc q h qh x r xr
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publishDate	2017
allfields	10.1016/j.isprsjprs.2017.11.007 doi (DE-627)ELV00022622X (ELSEVIER)S0924-2716(17)30344-1 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Mao, Qingzhou verfasserin aut A rigorous fastener inspection approach for high-speed railway from structured light sensors 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance. High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction Cui, Hao verfasserin (orcid)0000-0002-5121-3669 aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ren, Xiaochun verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 143, Seite 249-267 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:143 pages:249-267 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO 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_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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 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_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 143 249-267
spelling	10.1016/j.isprsjprs.2017.11.007 doi (DE-627)ELV00022622X (ELSEVIER)S0924-2716(17)30344-1 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Mao, Qingzhou verfasserin aut A rigorous fastener inspection approach for high-speed railway from structured light sensors 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance. High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction Cui, Hao verfasserin (orcid)0000-0002-5121-3669 aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ren, Xiaochun verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 143, Seite 249-267 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:143 pages:249-267 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO 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_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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 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_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 143 249-267
allfields_unstemmed	10.1016/j.isprsjprs.2017.11.007 doi (DE-627)ELV00022622X (ELSEVIER)S0924-2716(17)30344-1 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Mao, Qingzhou verfasserin aut A rigorous fastener inspection approach for high-speed railway from structured light sensors 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance. High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction Cui, Hao verfasserin (orcid)0000-0002-5121-3669 aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ren, Xiaochun verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 143, Seite 249-267 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:143 pages:249-267 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO 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_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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 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_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 143 249-267
allfieldsGer	10.1016/j.isprsjprs.2017.11.007 doi (DE-627)ELV00022622X (ELSEVIER)S0924-2716(17)30344-1 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Mao, Qingzhou verfasserin aut A rigorous fastener inspection approach for high-speed railway from structured light sensors 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance. High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction Cui, Hao verfasserin (orcid)0000-0002-5121-3669 aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ren, Xiaochun verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 143, Seite 249-267 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:143 pages:249-267 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO 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_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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 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_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 143 249-267
allfieldsSound	10.1016/j.isprsjprs.2017.11.007 doi (DE-627)ELV00022622X (ELSEVIER)S0924-2716(17)30344-1 DE-627 ger DE-627 rda eng 550 DE-600 38.73 bkl 74.41 bkl Mao, Qingzhou verfasserin aut A rigorous fastener inspection approach for high-speed railway from structured light sensors 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance. High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction Cui, Hao verfasserin (orcid)0000-0002-5121-3669 aut Hu, Qingwu verfasserin (orcid)0000-0003-0866-6678 aut Ren, Xiaochun verfasserin aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 143, Seite 249-267 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:143 pages:249-267 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO 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_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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 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_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.73 Geodäsie 74.41 Luftaufnahmen Photogrammetrie AR 143 249-267
language	English
source	Enthalten in ISPRS journal of photogrammetry and remote sensing 143, Seite 249-267 volume:143 pages:249-267
sourceStr	Enthalten in ISPRS journal of photogrammetry and remote sensing 143, Seite 249-267 volume:143 pages:249-267
format_phy_str_mv	Article
bklname	Geodäsie Luftaufnahmen Photogrammetrie
institution	findex.gbv.de
topic_facet	High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction
dewey-raw	550
isfreeaccess_bool	false
container_title	ISPRS journal of photogrammetry and remote sensing
authorswithroles_txt_mv	Mao, Qingzhou @@aut@@ Cui, Hao @@aut@@ Hu, Qingwu @@aut@@ Ren, Xiaochun @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	320504557
dewey-sort	3550
id	ELV00022622X
language_de	englisch
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author	Mao, Qingzhou
spellingShingle	Mao, Qingzhou ddc 550 bkl 38.73 bkl 74.41 misc High-speed railway misc Fastener inspection misc Structured light sensor misc Dense point cloud misc Decision tree misc Centerline extraction A rigorous fastener inspection approach for high-speed railway from structured light sensors
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topic_title	550 DE-600 38.73 bkl 74.41 bkl A rigorous fastener inspection approach for high-speed railway from structured light sensors High-speed railway Fastener inspection Structured light sensor Dense point cloud Decision tree Centerline extraction
topic	ddc 550 bkl 38.73 bkl 74.41 misc High-speed railway misc Fastener inspection misc Structured light sensor misc Dense point cloud misc Decision tree misc Centerline extraction
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title	A rigorous fastener inspection approach for high-speed railway from structured light sensors
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title_full	A rigorous fastener inspection approach for high-speed railway from structured light sensors
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title_sort	a rigorous fastener inspection approach for high-speed railway from structured light sensors
title_auth	A rigorous fastener inspection approach for high-speed railway from structured light sensors
abstract	Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
abstractGer	Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
abstract_unstemmed	Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
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title_short	A rigorous fastener inspection approach for high-speed railway from structured light sensors
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author2	Cui, Hao Hu, Qingwu Ren, Xiaochun
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up_date	2024-07-06T17:15:48.486Z
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Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. 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A rigorous fastener inspection approach for high-speed railway from structured light sensors

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