Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, a...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jingwei Cao [verfasserIn] Chuanxue Song [verfasserIn] Silun Peng [verfasserIn] Feng Xiao [verfasserIn] Shixin Song [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 19(2019), 18, p 4021
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:18, p 4021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s19184021

Katalog-ID:	DOAJ023914114

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520			\|a Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
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allfields	10.3390/s19184021 doi (DE-627)DOAJ023914114 (DE-599)DOAJ9c7e0971186c48ecabad333bae8bf10e DE-627 ger DE-627 rakwb eng TP1-1185 Jingwei Cao verfasserin aut Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition Chemical technology Chuanxue Song verfasserin aut Silun Peng verfasserin aut Feng Xiao verfasserin aut Shixin Song verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 4021 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 4021 https://doi.org/10.3390/s19184021 kostenfrei https://doaj.org/article/9c7e0971186c48ecabad333bae8bf10e kostenfrei https://www.mdpi.com/1424-8220/19/18/4021 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 4021
spelling	10.3390/s19184021 doi (DE-627)DOAJ023914114 (DE-599)DOAJ9c7e0971186c48ecabad333bae8bf10e DE-627 ger DE-627 rakwb eng TP1-1185 Jingwei Cao verfasserin aut Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition Chemical technology Chuanxue Song verfasserin aut Silun Peng verfasserin aut Feng Xiao verfasserin aut Shixin Song verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 4021 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 4021 https://doi.org/10.3390/s19184021 kostenfrei https://doaj.org/article/9c7e0971186c48ecabad333bae8bf10e kostenfrei https://www.mdpi.com/1424-8220/19/18/4021 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 4021
allfields_unstemmed	10.3390/s19184021 doi (DE-627)DOAJ023914114 (DE-599)DOAJ9c7e0971186c48ecabad333bae8bf10e DE-627 ger DE-627 rakwb eng TP1-1185 Jingwei Cao verfasserin aut Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition Chemical technology Chuanxue Song verfasserin aut Silun Peng verfasserin aut Feng Xiao verfasserin aut Shixin Song verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 4021 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 4021 https://doi.org/10.3390/s19184021 kostenfrei https://doaj.org/article/9c7e0971186c48ecabad333bae8bf10e kostenfrei https://www.mdpi.com/1424-8220/19/18/4021 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 4021
allfieldsGer	10.3390/s19184021 doi (DE-627)DOAJ023914114 (DE-599)DOAJ9c7e0971186c48ecabad333bae8bf10e DE-627 ger DE-627 rakwb eng TP1-1185 Jingwei Cao verfasserin aut Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition Chemical technology Chuanxue Song verfasserin aut Silun Peng verfasserin aut Feng Xiao verfasserin aut Shixin Song verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 4021 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 4021 https://doi.org/10.3390/s19184021 kostenfrei https://doaj.org/article/9c7e0971186c48ecabad333bae8bf10e kostenfrei https://www.mdpi.com/1424-8220/19/18/4021 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 4021
allfieldsSound	10.3390/s19184021 doi (DE-627)DOAJ023914114 (DE-599)DOAJ9c7e0971186c48ecabad333bae8bf10e DE-627 ger DE-627 rakwb eng TP1-1185 Jingwei Cao verfasserin aut Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition Chemical technology Chuanxue Song verfasserin aut Silun Peng verfasserin aut Feng Xiao verfasserin aut Shixin Song verfasserin aut In Sensors MDPI AG, 2003 19(2019), 18, p 4021 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:18, p 4021 https://doi.org/10.3390/s19184021 kostenfrei https://doaj.org/article/9c7e0971186c48ecabad333bae8bf10e kostenfrei https://www.mdpi.com/1424-8220/19/18/4021 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 18, p 4021
language	English
source	In Sensors 19(2019), 18, p 4021 volume:19 year:2019 number:18, p 4021
sourceStr	In Sensors 19(2019), 18, p 4021 volume:19 year:2019 number:18, p 4021
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institution	findex.gbv.de
topic_facet	driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition Chemical technology
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authorswithroles_txt_mv	Jingwei Cao @@aut@@ Chuanxue Song @@aut@@ Silun Peng @@aut@@ Feng Xiao @@aut@@ Shixin Song @@aut@@
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callnumber-first	T - Technology
author	Jingwei Cao
spellingShingle	Jingwei Cao misc TP1-1185 misc driving assistance misc intelligent vehicles misc traffic sign detection misc convolutional neural network misc traffic sign recognition misc Chemical technology Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
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topic_title	TP1-1185 Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles driving assistance intelligent vehicles traffic sign detection convolutional neural network traffic sign recognition
topic	misc TP1-1185 misc driving assistance misc intelligent vehicles misc traffic sign detection misc convolutional neural network misc traffic sign recognition misc Chemical technology
topic_unstemmed	misc TP1-1185 misc driving assistance misc intelligent vehicles misc traffic sign detection misc convolutional neural network misc traffic sign recognition misc Chemical technology
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title	Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
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title_full	Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
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author_browse	Jingwei Cao Chuanxue Song Silun Peng Feng Xiao Shixin Song
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doi_str_mv	10.3390/s19184021
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title_sort	improved traffic sign detection and recognition algorithm for intelligent vehicles
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title_auth	Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
abstract	Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
abstractGer	Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
abstract_unstemmed	Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
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title_short	Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
url	https://doi.org/10.3390/s19184021 https://doaj.org/article/9c7e0971186c48ecabad333bae8bf10e https://www.mdpi.com/1424-8220/19/18/4021 https://doaj.org/toc/1424-8220
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author2	Chuanxue Song Silun Peng Feng Xiao Shixin Song
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doi_str	10.3390/s19184021
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up_date	2024-07-03T20:10:16.131Z
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