Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Qiaoning [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Biosensors: A novel approach to and recent discovery in detection of cytokines - Mobed, Ahmad ELSEVIER, 2020, an international research journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:116 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.autcon.2020.103199 |
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ELV05050455X |
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| 520 | |a Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. | ||
| 520 | |a Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. | ||
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10.1016/j.autcon.2020.103199 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001027.pica (DE-627)ELV05050455X (ELSEVIER)S0926-5805(19)31600-0 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Yang, Qiaoning verfasserin aut Deep convolution neural network-based transfer learning method for civil infrastructure crack detection 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Shi, Weimin oth Chen, Juan oth Lin, Weiguo oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:116 year:2020 pages:0 https://doi.org/10.1016/j.autcon.2020.103199 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 116 2020 0 |
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10.1016/j.autcon.2020.103199 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001027.pica (DE-627)ELV05050455X (ELSEVIER)S0926-5805(19)31600-0 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Yang, Qiaoning verfasserin aut Deep convolution neural network-based transfer learning method for civil infrastructure crack detection 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Shi, Weimin oth Chen, Juan oth Lin, Weiguo oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:116 year:2020 pages:0 https://doi.org/10.1016/j.autcon.2020.103199 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 116 2020 0 |
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10.1016/j.autcon.2020.103199 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001027.pica (DE-627)ELV05050455X (ELSEVIER)S0926-5805(19)31600-0 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Yang, Qiaoning verfasserin aut Deep convolution neural network-based transfer learning method for civil infrastructure crack detection 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Shi, Weimin oth Chen, Juan oth Lin, Weiguo oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:116 year:2020 pages:0 https://doi.org/10.1016/j.autcon.2020.103199 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 116 2020 0 |
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10.1016/j.autcon.2020.103199 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001027.pica (DE-627)ELV05050455X (ELSEVIER)S0926-5805(19)31600-0 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Yang, Qiaoning verfasserin aut Deep convolution neural network-based transfer learning method for civil infrastructure crack detection 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Shi, Weimin oth Chen, Juan oth Lin, Weiguo oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:116 year:2020 pages:0 https://doi.org/10.1016/j.autcon.2020.103199 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 116 2020 0 |
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10.1016/j.autcon.2020.103199 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001027.pica (DE-627)ELV05050455X (ELSEVIER)S0926-5805(19)31600-0 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Yang, Qiaoning verfasserin aut Deep convolution neural network-based transfer learning method for civil infrastructure crack detection 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. Shi, Weimin oth Chen, Juan oth Lin, Weiguo oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:116 year:2020 pages:0 https://doi.org/10.1016/j.autcon.2020.103199 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 116 2020 0 |
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Biosensors: A novel approach to and recent discovery in detection of cytokines |
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Deep convolution neural network-based transfer learning method for civil infrastructure crack detection |
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Deep convolution neural network-based transfer learning method for civil infrastructure crack detection |
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Yang, Qiaoning |
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Biosensors: A novel approach to and recent discovery in detection of cytokines |
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deep convolution neural network-based transfer learning method for civil infrastructure crack detection |
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Deep convolution neural network-based transfer learning method for civil infrastructure crack detection |
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Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. |
| abstractGer |
Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. |
| abstract_unstemmed |
Crack detection is critical to guaranteeing safety of bridges, highway and other infrastructures. The deep convolution neural network (DCNN) makes it possible to efficiently and accurately implement image classification, and the accumulated knowledge of DCNN in other domains can be reused for crack detection. In this paper, we propose a transfer learning method based on DCNN to detect cracks. The proposed method models the knowledge learned by DCNN and transfers three kinds of knowledge from other research achievements: sample knowledge, model knowledge and parameter knowledge. New fully connected layers have emerged in the Visual Geometry Group (VGG) network as a new learning framework for crack detection. The performance and validity of the proposed method are verified. Compared with other detection methods, the proposed method can detect many kinds of cracks with a high detection accuracy. The detection accuracy for CCIC [24] is 99.83%, that for BCD [25] is 99.72%, and that for SDNET [45] is 97.07%. The accumulated knowledge in this method can also be transferred to other research work. |
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Deep convolution neural network-based transfer learning method for civil infrastructure crack detection |
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https://doi.org/10.1016/j.autcon.2020.103199 |
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Shi, Weimin Chen, Juan Lin, Weiguo |
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