Traffic signs detection and recognition systems by light-weight multi-stage network
Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hou, Mingzheng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 12 vom: 02. März, Seite 16155-16169 |
|---|---|
| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:12 ; day:02 ; month:03 ; pages:16155-16169 |
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| DOI / URN: |
10.1007/s11042-022-12201-x |
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OLC2078580848 |
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| 520 | |a Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. | ||
| 650 | 4 | |a Advanced Driver Assistance System (ADAS) | |
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| 650 | 4 | |a Traffic signs tracking | |
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| 700 | 1 | |a Zhang, Xin |0 (orcid)0000-0002-4133-6964 |4 aut | |
| 700 | 1 | |a Chen, Yang |4 aut | |
| 700 | 1 | |a Dong, Penglin |4 aut | |
| 700 | 1 | |a Feng, Ziliang |4 aut | |
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10.1007/s11042-022-12201-x doi (DE-627)OLC2078580848 (DE-He213)s11042-022-12201-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Hou, Mingzheng verfasserin aut Traffic signs detection and recognition systems by light-weight multi-stage network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. Advanced Driver Assistance System (ADAS) Traffic signs recognition Traffic signs tracking Deep learning Zhang, Xin (orcid)0000-0002-4133-6964 aut Chen, Yang aut Dong, Penglin aut Feng, Ziliang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 12 vom: 02. März, Seite 16155-16169 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:12 day:02 month:03 pages:16155-16169 https://doi.org/10.1007/s11042-022-12201-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 12 02 03 16155-16169 |
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10.1007/s11042-022-12201-x doi (DE-627)OLC2078580848 (DE-He213)s11042-022-12201-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Hou, Mingzheng verfasserin aut Traffic signs detection and recognition systems by light-weight multi-stage network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. Advanced Driver Assistance System (ADAS) Traffic signs recognition Traffic signs tracking Deep learning Zhang, Xin (orcid)0000-0002-4133-6964 aut Chen, Yang aut Dong, Penglin aut Feng, Ziliang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 12 vom: 02. März, Seite 16155-16169 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:12 day:02 month:03 pages:16155-16169 https://doi.org/10.1007/s11042-022-12201-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 12 02 03 16155-16169 |
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10.1007/s11042-022-12201-x doi (DE-627)OLC2078580848 (DE-He213)s11042-022-12201-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Hou, Mingzheng verfasserin aut Traffic signs detection and recognition systems by light-weight multi-stage network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. Advanced Driver Assistance System (ADAS) Traffic signs recognition Traffic signs tracking Deep learning Zhang, Xin (orcid)0000-0002-4133-6964 aut Chen, Yang aut Dong, Penglin aut Feng, Ziliang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 12 vom: 02. März, Seite 16155-16169 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:12 day:02 month:03 pages:16155-16169 https://doi.org/10.1007/s11042-022-12201-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 12 02 03 16155-16169 |
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10.1007/s11042-022-12201-x doi (DE-627)OLC2078580848 (DE-He213)s11042-022-12201-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Hou, Mingzheng verfasserin aut Traffic signs detection and recognition systems by light-weight multi-stage network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. Advanced Driver Assistance System (ADAS) Traffic signs recognition Traffic signs tracking Deep learning Zhang, Xin (orcid)0000-0002-4133-6964 aut Chen, Yang aut Dong, Penglin aut Feng, Ziliang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 12 vom: 02. März, Seite 16155-16169 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:12 day:02 month:03 pages:16155-16169 https://doi.org/10.1007/s11042-022-12201-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 12 02 03 16155-16169 |
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10.1007/s11042-022-12201-x doi (DE-627)OLC2078580848 (DE-He213)s11042-022-12201-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Hou, Mingzheng verfasserin aut Traffic signs detection and recognition systems by light-weight multi-stage network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. Advanced Driver Assistance System (ADAS) Traffic signs recognition Traffic signs tracking Deep learning Zhang, Xin (orcid)0000-0002-4133-6964 aut Chen, Yang aut Dong, Penglin aut Feng, Ziliang aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 12 vom: 02. März, Seite 16155-16169 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:12 day:02 month:03 pages:16155-16169 https://doi.org/10.1007/s11042-022-12201-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 12 02 03 16155-16169 |
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Traffic signs detection and recognition systems by light-weight multi-stage network |
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Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| title_short |
Traffic signs detection and recognition systems by light-weight multi-stage network |
| url |
https://doi.org/10.1007/s11042-022-12201-x |
| remote_bool |
false |
| author2 |
Zhang, Xin Chen, Yang Dong, Penglin Feng, Ziliang |
| author2Str |
Zhang, Xin Chen, Yang Dong, Penglin Feng, Ziliang |
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| doi_str |
10.1007/s11042-022-12201-x |
| up_date |
2024-07-03T21:08:25.060Z |
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