YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5)
Driving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the diverse and differentiated features of typographic fonts...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2024-01-01
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| Series: | Cognitive Robotics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266724132400003X |
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| author | Dehua Liu Yongqin Tian Yibo Xu Wenyi Zhao Xipeng Pan Xu Ji Mu Yang Huihua Yang |
| author_facet | Dehua Liu Yongqin Tian Yibo Xu Wenyi Zhao Xipeng Pan Xu Ji Mu Yang Huihua Yang |
| author_sort | Dehua Liu |
| collection | DOAJ |
| description | Driving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the diverse and differentiated features of typographic fonts, simultaneously extracting comprehensive characteristics is an extremely challenging task. To effectively break through these performance degradation issues, a multi-scale tire sidewall text region detection algorithm based on YOLOv5 is introduced, called YOLOT, which fuses comprehensive feature information in both width and depth directions. In this study, we firstly propose the Width and Depth Awareness (WDA) module in the text region detection field and successfully integrated it with the FPN structure to form the WDA-FPN. The purpose of WDA-FPN is to empower the network to capture multi-scale and multi-shape features in images, thereby augmenting the algorithm’s abstraction and representation of image features and concurrently boosting its robustness and generalization performance. Experimental findings indicate that, compared to the primary algorithm, YOLOT achieves significant improvement in accuracy, providing a higher detection reliability. The dataset and code for the paper are available at: https://github.com/Cloude-dehua/YOLOT. |
| format | Article |
| id | doaj-art-ac11d1bcc1e043b98a1a6fc3ee64e550 |
| institution | Kabale University |
| issn | 2667-2413 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj-art-ac11d1bcc1e043b98a1a6fc3ee64e5502024-12-15T06:17:51ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132024-01-0147487YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5)Dehua Liu0Yongqin Tian1Yibo Xu2Wenyi Zhao3Xipeng Pan4Xu Ji5Mu Yang6Huihua Yang7School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, 453003, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, ChinaTechmach (Beijing) Industrial Technology Co. Ltd, Beijing, 102600, ChinaCorresponding author.; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaDriving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the diverse and differentiated features of typographic fonts, simultaneously extracting comprehensive characteristics is an extremely challenging task. To effectively break through these performance degradation issues, a multi-scale tire sidewall text region detection algorithm based on YOLOv5 is introduced, called YOLOT, which fuses comprehensive feature information in both width and depth directions. In this study, we firstly propose the Width and Depth Awareness (WDA) module in the text region detection field and successfully integrated it with the FPN structure to form the WDA-FPN. The purpose of WDA-FPN is to empower the network to capture multi-scale and multi-shape features in images, thereby augmenting the algorithm’s abstraction and representation of image features and concurrently boosting its robustness and generalization performance. Experimental findings indicate that, compared to the primary algorithm, YOLOT achieves significant improvement in accuracy, providing a higher detection reliability. The dataset and code for the paper are available at: https://github.com/Cloude-dehua/YOLOT.http://www.sciencedirect.com/science/article/pii/S266724132400003XTire sidewallMulti-scaleDiverse charactersYOLOCharacter detection |
| spellingShingle | Dehua Liu Yongqin Tian Yibo Xu Wenyi Zhao Xipeng Pan Xu Ji Mu Yang Huihua Yang YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) Cognitive Robotics Tire sidewall Multi-scale Diverse characters YOLO Character detection |
| title | YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) |
| title_full | YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) |
| title_fullStr | YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) |
| title_full_unstemmed | YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) |
| title_short | YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) |
| title_sort | yolot multi scale and diverse tire sidewall text region detection based on you only look once yolov5 |
| topic | Tire sidewall Multi-scale Diverse characters YOLO Character detection |
| url | http://www.sciencedirect.com/science/article/pii/S266724132400003X |
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