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Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4
Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617714/ https://www.ncbi.nlm.nih.gov/pubmed/36317077 http://dx.doi.org/10.1155/2022/5297605 |
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author | Wang, Yongjie Bai, Miaoyuan Wang, Mingzhi Zhao, Fengfeng Guo, Jifeng |
author_facet | Wang, Yongjie Bai, Miaoyuan Wang, Mingzhi Zhao, Fengfeng Guo, Jifeng |
author_sort | Wang, Yongjie |
collection | PubMed |
description | Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection. |
format | Online Article Text |
id | pubmed-9617714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-96177142022-10-30 Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 Wang, Yongjie Bai, Miaoyuan Wang, Mingzhi Zhao, Fengfeng Guo, Jifeng Comput Intell Neurosci Research Article Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection. Hindawi 2022-10-22 /pmc/articles/PMC9617714/ /pubmed/36317077 http://dx.doi.org/10.1155/2022/5297605 Text en Copyright © 2022 Yongjie Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Yongjie Bai, Miaoyuan Wang, Mingzhi Zhao, Fengfeng Guo, Jifeng Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 |
title | Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 |
title_full | Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 |
title_fullStr | Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 |
title_full_unstemmed | Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 |
title_short | Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4 |
title_sort | multiscale traffic sign detection method in complex environment based on yolov4 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617714/ https://www.ncbi.nlm.nih.gov/pubmed/36317077 http://dx.doi.org/10.1155/2022/5297605 |
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