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
Autor*in: |
Bayoudh, Khaled [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
---|
Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 51(2020), 1 vom: 06. Aug., Seite 124-142 |
---|---|
Übergeordnetes Werk: |
volume:51 ; year:2020 ; number:1 ; day:06 ; month:08 ; pages:124-142 |
Links: |
---|
DOI / URN: |
10.1007/s10489-020-01801-5 |
---|
Katalog-ID: |
OLC2122314788 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC2122314788 | ||
003 | DE-627 | ||
005 | 20230505062241.0 | ||
007 | tu | ||
008 | 230505s2020 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s10489-020-01801-5 |2 doi | |
035 | |a (DE-627)OLC2122314788 | ||
035 | |a (DE-He213)s10489-020-01801-5-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q VZ |
100 | 1 | |a Bayoudh, Khaled |e verfasserin |0 (orcid)0000-0002-1148-4800 |4 aut | |
245 | 1 | 0 | |a Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2020 | ||
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 | |
650 | 4 | |a Traffic sign recognition | |
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 | |
773 | 0 | 8 | |i Enthalten in |t Applied intelligence |d Springer US, 1991 |g 51(2020), 1 vom: 06. Aug., Seite 124-142 |w (DE-627)130990515 |w (DE-600)1080229-0 |w (DE-576)029154286 |x 0924-669X |7 nnns |
773 | 1 | 8 | |g volume:51 |g year:2020 |g number:1 |g day:06 |g month:08 |g pages:124-142 |
856 | 4 | 1 | |u https://doi.org/10.1007/s10489-020-01801-5 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-MAT | ||
951 | |a AR | ||
952 | |d 51 |j 2020 |e 1 |b 06 |c 08 |h 124-142 |
author_variant |
k b kb f h fh a m am |
---|---|
matchkey_str |
article:0924669X:2020----::rnfrerigaehbi23cnotafcineontoadeatcodeetoapid |
hierarchy_sort_str |
2020 |
publishDate |
2020 |
allfields |
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 |
spelling |
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 |
allfields_unstemmed |
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 |
allfieldsGer |
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 |
allfieldsSound |
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 |
language |
English |
source |
Enthalten in Applied intelligence 51(2020), 1 vom: 06. Aug., Seite 124-142 volume:51 year:2020 number:1 day:06 month:08 pages:124-142 |
sourceStr |
Enthalten in Applied intelligence 51(2020), 1 vom: 06. Aug., Seite 124-142 volume:51 year:2020 number:1 day:06 month:08 pages:124-142 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Applied intelligence |
authorswithroles_txt_mv |
Bayoudh, Khaled @@aut@@ Hamdaoui, Fayçal @@aut@@ Mtibaa, Abdellatif @@aut@@ |
publishDateDaySort_date |
2020-08-06T00:00:00Z |
hierarchy_top_id |
130990515 |
dewey-sort |
14 |
id |
OLC2122314788 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2122314788</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505062241.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-020-01801-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2122314788</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-020-01801-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bayoudh, Khaled</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-1148-4800</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic sign recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantic road detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transfer learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid 2D-3D CNN models</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hamdaoui, Fayçal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mtibaa, Abdellatif</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">51(2020), 1 vom: 06. Aug., Seite 124-142</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:06</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:124-142</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-020-01801-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">51</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">06</subfield><subfield code="c">08</subfield><subfield code="h">124-142</subfield></datafield></record></collection>
|
author |
Bayoudh, Khaled |
spellingShingle |
Bayoudh, Khaled ddc 004 misc Deep learning misc Traffic sign recognition misc Semantic road detection misc Transfer learning misc Hybrid 2D-3D CNN models Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
authorStr |
Bayoudh, Khaled |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)130990515 |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0924-669X |
topic_title |
004 VZ Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems Deep learning Traffic sign recognition Semantic road detection Transfer learning Hybrid 2D-3D CNN models |
topic |
ddc 004 misc Deep learning misc Traffic sign recognition misc Semantic road detection misc Transfer learning misc Hybrid 2D-3D CNN models |
topic_unstemmed |
ddc 004 misc Deep learning misc Traffic sign recognition misc Semantic road detection misc Transfer learning misc Hybrid 2D-3D CNN models |
topic_browse |
ddc 004 misc Deep learning misc Traffic sign recognition misc Semantic road detection misc Transfer learning misc Hybrid 2D-3D CNN models |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Applied intelligence |
hierarchy_parent_id |
130990515 |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
Applied intelligence |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 |
title |
Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
ctrlnum |
(DE-627)OLC2122314788 (DE-He213)s10489-020-01801-5-p |
title_full |
Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
author_sort |
Bayoudh, Khaled |
journal |
Applied intelligence |
journalStr |
Applied intelligence |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
container_start_page |
124 |
author_browse |
Bayoudh, Khaled Hamdaoui, Fayçal Mtibaa, Abdellatif |
container_volume |
51 |
class |
004 VZ |
format_se |
Aufsätze |
author-letter |
Bayoudh, Khaled |
doi_str_mv |
10.1007/s10489-020-01801-5 |
normlink |
(ORCID)0000-0002-1148-4800 |
normlink_prefix_str_mv |
(orcid)0000-0002-1148-4800 |
dewey-full |
004 |
title_sort |
transfer learning based hybrid 2d-3d cnn for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
title_auth |
Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
abstract |
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 |
abstractGer |
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 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT |
container_issue |
1 |
title_short |
Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems |
url |
https://doi.org/10.1007/s10489-020-01801-5 |
remote_bool |
false |
author2 |
Hamdaoui, Fayçal Mtibaa, Abdellatif |
author2Str |
Hamdaoui, Fayçal Mtibaa, Abdellatif |
ppnlink |
130990515 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10489-020-01801-5 |
up_date |
2024-07-04T09:45:21.062Z |
_version_ |
1803641234201247744 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2122314788</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505062241.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-020-01801-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2122314788</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-020-01801-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bayoudh, Khaled</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-1148-4800</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic sign recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantic road detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transfer learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid 2D-3D CNN models</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hamdaoui, Fayçal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mtibaa, Abdellatif</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">51(2020), 1 vom: 06. Aug., Seite 124-142</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:06</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:124-142</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-020-01801-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">51</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">06</subfield><subfield code="c">08</subfield><subfield code="h">124-142</subfield></datafield></record></collection>
|
score |
7.3999033 |