Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm

Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a...
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Autor*in:	Jian Han [verfasserIn] Yaping Liao [verfasserIn] Junyou Zhang [verfasserIn] Shufeng Wang [verfasserIn] Sixian Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	autonomous vehicle target detection multi-sensors fusion YOLO

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 6(2018), 10, p 213
Übergeordnetes Werk:	volume:6 ; year:2018 ; number:10, p 213

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math6100213

Katalog-ID:	DOAJ044690592

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allfields	10.3390/math6100213 doi (DE-627)DOAJ044690592 (DE-599)DOAJa05377caf7a34fdba6c5d266cca46d45 DE-627 ger DE-627 rakwb eng QA1-939 Jian Han verfasserin aut Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect. autonomous vehicle target detection multi-sensors fusion YOLO Mathematics Yaping Liao verfasserin aut Junyou Zhang verfasserin aut Shufeng Wang verfasserin aut Sixian Li verfasserin aut In Mathematics MDPI AG, 2013 6(2018), 10, p 213 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:6 year:2018 number:10, p 213 https://doi.org/10.3390/math6100213 kostenfrei https://doaj.org/article/a05377caf7a34fdba6c5d266cca46d45 kostenfrei http://www.mdpi.com/2227-7390/6/10/213 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 10, p 213
spelling	10.3390/math6100213 doi (DE-627)DOAJ044690592 (DE-599)DOAJa05377caf7a34fdba6c5d266cca46d45 DE-627 ger DE-627 rakwb eng QA1-939 Jian Han verfasserin aut Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect. autonomous vehicle target detection multi-sensors fusion YOLO Mathematics Yaping Liao verfasserin aut Junyou Zhang verfasserin aut Shufeng Wang verfasserin aut Sixian Li verfasserin aut In Mathematics MDPI AG, 2013 6(2018), 10, p 213 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:6 year:2018 number:10, p 213 https://doi.org/10.3390/math6100213 kostenfrei https://doaj.org/article/a05377caf7a34fdba6c5d266cca46d45 kostenfrei http://www.mdpi.com/2227-7390/6/10/213 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 10, p 213
allfields_unstemmed	10.3390/math6100213 doi (DE-627)DOAJ044690592 (DE-599)DOAJa05377caf7a34fdba6c5d266cca46d45 DE-627 ger DE-627 rakwb eng QA1-939 Jian Han verfasserin aut Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect. autonomous vehicle target detection multi-sensors fusion YOLO Mathematics Yaping Liao verfasserin aut Junyou Zhang verfasserin aut Shufeng Wang verfasserin aut Sixian Li verfasserin aut In Mathematics MDPI AG, 2013 6(2018), 10, p 213 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:6 year:2018 number:10, p 213 https://doi.org/10.3390/math6100213 kostenfrei https://doaj.org/article/a05377caf7a34fdba6c5d266cca46d45 kostenfrei http://www.mdpi.com/2227-7390/6/10/213 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 10, p 213
allfieldsGer	10.3390/math6100213 doi (DE-627)DOAJ044690592 (DE-599)DOAJa05377caf7a34fdba6c5d266cca46d45 DE-627 ger DE-627 rakwb eng QA1-939 Jian Han verfasserin aut Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect. autonomous vehicle target detection multi-sensors fusion YOLO Mathematics Yaping Liao verfasserin aut Junyou Zhang verfasserin aut Shufeng Wang verfasserin aut Sixian Li verfasserin aut In Mathematics MDPI AG, 2013 6(2018), 10, p 213 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:6 year:2018 number:10, p 213 https://doi.org/10.3390/math6100213 kostenfrei https://doaj.org/article/a05377caf7a34fdba6c5d266cca46d45 kostenfrei http://www.mdpi.com/2227-7390/6/10/213 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 10, p 213
allfieldsSound	10.3390/math6100213 doi (DE-627)DOAJ044690592 (DE-599)DOAJa05377caf7a34fdba6c5d266cca46d45 DE-627 ger DE-627 rakwb eng QA1-939 Jian Han verfasserin aut Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect. autonomous vehicle target detection multi-sensors fusion YOLO Mathematics Yaping Liao verfasserin aut Junyou Zhang verfasserin aut Shufeng Wang verfasserin aut Sixian Li verfasserin aut In Mathematics MDPI AG, 2013 6(2018), 10, p 213 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:6 year:2018 number:10, p 213 https://doi.org/10.3390/math6100213 kostenfrei https://doaj.org/article/a05377caf7a34fdba6c5d266cca46d45 kostenfrei http://www.mdpi.com/2227-7390/6/10/213 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 10, p 213
language	English
source	In Mathematics 6(2018), 10, p 213 volume:6 year:2018 number:10, p 213
sourceStr	In Mathematics 6(2018), 10, p 213 volume:6 year:2018 number:10, p 213
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	autonomous vehicle target detection multi-sensors fusion YOLO Mathematics
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container_title	Mathematics
authorswithroles_txt_mv	Jian Han @@aut@@ Yaping Liao @@aut@@ Junyou Zhang @@aut@@ Shufeng Wang @@aut@@ Sixian Li @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
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At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. 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callnumber-first	Q - Science
author	Jian Han
spellingShingle	Jian Han misc QA1-939 misc autonomous vehicle misc target detection misc multi-sensors misc fusion misc YOLO misc Mathematics Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm
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topic_title	QA1-939 Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm autonomous vehicle target detection multi-sensors fusion YOLO
topic	misc QA1-939 misc autonomous vehicle misc target detection misc multi-sensors misc fusion misc YOLO misc Mathematics
topic_unstemmed	misc QA1-939 misc autonomous vehicle misc target detection misc multi-sensors misc fusion misc YOLO misc Mathematics
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title	Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm
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title_full	Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm
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title_sort	target fusion detection of lidar and camera based on the improved yolo algorithm
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title_auth	Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm
abstract	Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.
abstractGer	Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.
abstract_unstemmed	Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.
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title_short	Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm
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