An Improved YOLOv5 Algorithm for Vulnerable Road User Detection

The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, a...
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Autor*in:	Wei Yang [verfasserIn] Xiaolin Tang [verfasserIn] Kongming Jiang [verfasserIn] Yang Fu [verfasserIn] Xinling Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 7761, p 7761
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:7761, p 7761

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23187761

Katalog-ID:	DOAJ093282753

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520			\|a The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.
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allfields	10.3390/s23187761 doi (DE-627)DOAJ093282753 (DE-599)DOAJf9f77f142324488cac5f1ac010da0137 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Yang verfasserin aut An Improved YOLOv5 Algorithm for Vulnerable Road User Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection. improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system Chemical technology Xiaolin Tang verfasserin aut Kongming Jiang verfasserin aut Yang Fu verfasserin aut Xinling Zhang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7761, p 7761 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7761, p 7761 https://doi.org/10.3390/s23187761 kostenfrei https://doaj.org/article/f9f77f142324488cac5f1ac010da0137 kostenfrei https://www.mdpi.com/1424-8220/23/18/7761 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 23 2023 7761, p 7761
spelling	10.3390/s23187761 doi (DE-627)DOAJ093282753 (DE-599)DOAJf9f77f142324488cac5f1ac010da0137 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Yang verfasserin aut An Improved YOLOv5 Algorithm for Vulnerable Road User Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection. improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system Chemical technology Xiaolin Tang verfasserin aut Kongming Jiang verfasserin aut Yang Fu verfasserin aut Xinling Zhang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7761, p 7761 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7761, p 7761 https://doi.org/10.3390/s23187761 kostenfrei https://doaj.org/article/f9f77f142324488cac5f1ac010da0137 kostenfrei https://www.mdpi.com/1424-8220/23/18/7761 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 23 2023 7761, p 7761
allfields_unstemmed	10.3390/s23187761 doi (DE-627)DOAJ093282753 (DE-599)DOAJf9f77f142324488cac5f1ac010da0137 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Yang verfasserin aut An Improved YOLOv5 Algorithm for Vulnerable Road User Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection. improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system Chemical technology Xiaolin Tang verfasserin aut Kongming Jiang verfasserin aut Yang Fu verfasserin aut Xinling Zhang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7761, p 7761 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7761, p 7761 https://doi.org/10.3390/s23187761 kostenfrei https://doaj.org/article/f9f77f142324488cac5f1ac010da0137 kostenfrei https://www.mdpi.com/1424-8220/23/18/7761 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 23 2023 7761, p 7761
allfieldsGer	10.3390/s23187761 doi (DE-627)DOAJ093282753 (DE-599)DOAJf9f77f142324488cac5f1ac010da0137 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Yang verfasserin aut An Improved YOLOv5 Algorithm for Vulnerable Road User Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection. improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system Chemical technology Xiaolin Tang verfasserin aut Kongming Jiang verfasserin aut Yang Fu verfasserin aut Xinling Zhang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7761, p 7761 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7761, p 7761 https://doi.org/10.3390/s23187761 kostenfrei https://doaj.org/article/f9f77f142324488cac5f1ac010da0137 kostenfrei https://www.mdpi.com/1424-8220/23/18/7761 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 23 2023 7761, p 7761
allfieldsSound	10.3390/s23187761 doi (DE-627)DOAJ093282753 (DE-599)DOAJf9f77f142324488cac5f1ac010da0137 DE-627 ger DE-627 rakwb eng TP1-1185 Wei Yang verfasserin aut An Improved YOLOv5 Algorithm for Vulnerable Road User Detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection. improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system Chemical technology Xiaolin Tang verfasserin aut Kongming Jiang verfasserin aut Yang Fu verfasserin aut Xinling Zhang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 7761, p 7761 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:7761, p 7761 https://doi.org/10.3390/s23187761 kostenfrei https://doaj.org/article/f9f77f142324488cac5f1ac010da0137 kostenfrei https://www.mdpi.com/1424-8220/23/18/7761 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 23 2023 7761, p 7761
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source	In Sensors 23(2023), 7761, p 7761 volume:23 year:2023 number:7761, p 7761
sourceStr	In Sensors 23(2023), 7761, p 7761 volume:23 year:2023 number:7761, p 7761
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authorswithroles_txt_mv	Wei Yang @@aut@@ Xiaolin Tang @@aut@@ Kongming Jiang @@aut@@ Yang Fu @@aut@@ Xinling Zhang @@aut@@
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callnumber-first	T - Technology
author	Wei Yang
spellingShingle	Wei Yang misc TP1-1185 misc improved YOLOv5 algorithm misc overlapping targets misc small targets misc detection accuracy misc AEBS-VRU system misc Chemical technology An Improved YOLOv5 Algorithm for Vulnerable Road User Detection
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topic_title	TP1-1185 An Improved YOLOv5 Algorithm for Vulnerable Road User Detection improved YOLOv5 algorithm overlapping targets small targets detection accuracy AEBS-VRU system
topic	misc TP1-1185 misc improved YOLOv5 algorithm misc overlapping targets misc small targets misc detection accuracy misc AEBS-VRU system misc Chemical technology
topic_unstemmed	misc TP1-1185 misc improved YOLOv5 algorithm misc overlapping targets misc small targets misc detection accuracy misc AEBS-VRU system misc Chemical technology
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title	An Improved YOLOv5 Algorithm for Vulnerable Road User Detection
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title_full	An Improved YOLOv5 Algorithm for Vulnerable Road User Detection
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author_browse	Wei Yang Xiaolin Tang Kongming Jiang Yang Fu Xinling Zhang
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title_sort	improved yolov5 algorithm for vulnerable road user detection
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title_auth	An Improved YOLOv5 Algorithm for Vulnerable Road User Detection
abstract	The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.
abstractGer	The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.
abstract_unstemmed	The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.
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title_short	An Improved YOLOv5 Algorithm for Vulnerable Road User Detection
url	https://doi.org/10.3390/s23187761 https://doaj.org/article/f9f77f142324488cac5f1ac010da0137 https://www.mdpi.com/1424-8220/23/18/7761 https://doaj.org/toc/1424-8220
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An Improved YOLOv5 Algorithm for Vulnerable Road User Detection

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