Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems
Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increas...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bayoudh, Khaled [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 51(2020), 1 vom: 06. Aug., Seite 124-142 |
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| Übergeordnetes Werk: |
volume:51 ; year:2020 ; number:1 ; day:06 ; month:08 ; pages:124-142 |
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| DOI / URN: |
10.1007/s10489-020-01801-5 |
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OLC2122314788 |
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| 520 | |a Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. | ||
| 650 | 4 | |a Deep learning | |
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| 650 | 4 | |a Semantic road detection | |
| 650 | 4 | |a Transfer learning | |
| 650 | 4 | |a Hybrid 2D-3D CNN models | |
| 700 | 1 | |a Hamdaoui, Fayçal |4 aut | |
| 700 | 1 | |a Mtibaa, Abdellatif |4 aut | |
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10.1007/s10489-020-01801-5 doi (DE-627)OLC2122314788 (DE-He213)s10489-020-01801-5-p DE-627 ger DE-627 rakwb eng 004 VZ Bayoudh, Khaled verfasserin (orcid)0000-0002-1148-4800 aut Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models Hamdaoui, Fayçal aut Mtibaa, Abdellatif aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 06. Aug., Seite 124-142 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:06 month:08 pages:124-142 https://doi.org/10.1007/s10489-020-01801-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 06 08 124-142 |
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10.1007/s10489-020-01801-5 doi (DE-627)OLC2122314788 (DE-He213)s10489-020-01801-5-p DE-627 ger DE-627 rakwb eng 004 VZ Bayoudh, Khaled verfasserin (orcid)0000-0002-1148-4800 aut Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models Hamdaoui, Fayçal aut Mtibaa, Abdellatif aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 06. Aug., Seite 124-142 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:06 month:08 pages:124-142 https://doi.org/10.1007/s10489-020-01801-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 06 08 124-142 |
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10.1007/s10489-020-01801-5 doi (DE-627)OLC2122314788 (DE-He213)s10489-020-01801-5-p DE-627 ger DE-627 rakwb eng 004 VZ Bayoudh, Khaled verfasserin (orcid)0000-0002-1148-4800 aut Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models Hamdaoui, Fayçal aut Mtibaa, Abdellatif aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 06. Aug., Seite 124-142 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:06 month:08 pages:124-142 https://doi.org/10.1007/s10489-020-01801-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 06 08 124-142 |
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10.1007/s10489-020-01801-5 doi (DE-627)OLC2122314788 (DE-He213)s10489-020-01801-5-p DE-627 ger DE-627 rakwb eng 004 VZ Bayoudh, Khaled verfasserin (orcid)0000-0002-1148-4800 aut Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models Hamdaoui, Fayçal aut Mtibaa, Abdellatif aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 06. Aug., Seite 124-142 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:06 month:08 pages:124-142 https://doi.org/10.1007/s10489-020-01801-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 06 08 124-142 |
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10.1007/s10489-020-01801-5 doi (DE-627)OLC2122314788 (DE-He213)s10489-020-01801-5-p DE-627 ger DE-627 rakwb eng 004 VZ Bayoudh, Khaled verfasserin (orcid)0000-0002-1148-4800 aut Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models Hamdaoui, Fayçal aut Mtibaa, Abdellatif aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 06. Aug., Seite 124-142 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:06 month:08 pages:124-142 https://doi.org/10.1007/s10489-020-01801-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 06 08 124-142 |
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Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
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Hamdaoui, Fayçal Mtibaa, Abdellatif |
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