Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning
Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and m...
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| Autor*in: |
Dhawan, Kshitij [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2023), 17 vom: 04. März, Seite 26465-26480 |
|---|---|
| Übergeordnetes Werk: |
volume:82 ; year:2023 ; number:17 ; day:04 ; month:03 ; pages:26465-26480 |
| Links: |
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| DOI / URN: |
10.1007/s11042-023-14823-1 |
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OLC2144067007 |
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| 520 | |a Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. | ||
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10.1007/s11042-023-14823-1 doi (DE-627)OLC2144067007 (DE-He213)s11042-023-14823-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhawan, Kshitij verfasserin aut Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. ADAS YOLO v3 YOLO v4-tiny Traffic signs recognition Customised CNN Image augmentation R, Srinivasa Perumal aut R. K., Nadesh (orcid)0000-0001-5754-519X aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 17 vom: 04. März, Seite 26465-26480 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:17 day:04 month:03 pages:26465-26480 https://doi.org/10.1007/s11042-023-14823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 17 04 03 26465-26480 |
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10.1007/s11042-023-14823-1 doi (DE-627)OLC2144067007 (DE-He213)s11042-023-14823-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhawan, Kshitij verfasserin aut Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. ADAS YOLO v3 YOLO v4-tiny Traffic signs recognition Customised CNN Image augmentation R, Srinivasa Perumal aut R. K., Nadesh (orcid)0000-0001-5754-519X aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 17 vom: 04. März, Seite 26465-26480 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:17 day:04 month:03 pages:26465-26480 https://doi.org/10.1007/s11042-023-14823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 17 04 03 26465-26480 |
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10.1007/s11042-023-14823-1 doi (DE-627)OLC2144067007 (DE-He213)s11042-023-14823-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhawan, Kshitij verfasserin aut Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. ADAS YOLO v3 YOLO v4-tiny Traffic signs recognition Customised CNN Image augmentation R, Srinivasa Perumal aut R. K., Nadesh (orcid)0000-0001-5754-519X aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 17 vom: 04. März, Seite 26465-26480 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:17 day:04 month:03 pages:26465-26480 https://doi.org/10.1007/s11042-023-14823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 17 04 03 26465-26480 |
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10.1007/s11042-023-14823-1 doi (DE-627)OLC2144067007 (DE-He213)s11042-023-14823-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhawan, Kshitij verfasserin aut Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. ADAS YOLO v3 YOLO v4-tiny Traffic signs recognition Customised CNN Image augmentation R, Srinivasa Perumal aut R. K., Nadesh (orcid)0000-0001-5754-519X aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 17 vom: 04. März, Seite 26465-26480 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:17 day:04 month:03 pages:26465-26480 https://doi.org/10.1007/s11042-023-14823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 17 04 03 26465-26480 |
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10.1007/s11042-023-14823-1 doi (DE-627)OLC2144067007 (DE-He213)s11042-023-14823-1-p DE-627 ger DE-627 rakwb eng 070 004 VZ Dhawan, Kshitij verfasserin aut Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. ADAS YOLO v3 YOLO v4-tiny Traffic signs recognition Customised CNN Image augmentation R, Srinivasa Perumal aut R. K., Nadesh (orcid)0000-0001-5754-519X aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 17 vom: 04. März, Seite 26465-26480 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:17 day:04 month:03 pages:26465-26480 https://doi.org/10.1007/s11042-023-14823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 17 04 03 26465-26480 |
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Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning |
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Dhawan, Kshitij |
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Dhawan, Kshitij R, Srinivasa Perumal R. K., Nadesh |
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Dhawan, Kshitij |
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10.1007/s11042-023-14823-1 |
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identification of traffic signs for advanced driving assistance systems in smart cities using deep learning |
| title_auth |
Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning |
| abstract |
Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The ability of Advanced Driving Assistance Systems (ADAS) is to identify and understand all objects around the vehicle under varying driving conditions and environmental factors is critical. Today’s vehicles are equipped with advanced driving assistance systems that make driving safer and more comfortable. A camera mounted on the car helps the system recognise and detect traffic signs and alerts the driver about various road conditions, like if construction work is ahead or if speed limits have changed. The goal is to identify the traffic sign and process the image in a minimal processing time. A custom convolutional neural network model is used to classify the traffic signs with higher accuracy than the existing models. Image augmentation techniques are used to expand the dataset artificially, and that allows one to learn how the image looks from different perspectives, such as when viewed from different angles or when it looks blurry due to poor weather conditions. The algorithms used to detect traffic signs are YOLO v3 and YOLO v4-tiny. The proposed solution for detecting a specific set of traffic signs performed well, with an accuracy rate of 95.85%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning |
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https://doi.org/10.1007/s11042-023-14823-1 |
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