Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign
Traffic sign recognition plays a crucial role in the intelligent vehicle’s environment perception system. However, due to varying weather conditions, illumination, and complicated backgrounds, recognizing traffic signs becomes very challenging. A novel lightweight detection model based on...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10278405/ |
| _version_ | 1827789246129242112 |
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| author | Weizhen Song Shahrel Azmin Suandi |
| author_facet | Weizhen Song Shahrel Azmin Suandi |
| author_sort | Weizhen Song |
| collection | DOAJ |
| description | Traffic sign recognition plays a crucial role in the intelligent vehicle’s environment perception system. However, due to varying weather conditions, illumination, and complicated backgrounds, recognizing traffic signs becomes very challenging. A novel lightweight detection model based on YOLOv5s, namely Sign-YOLO, is proposed to overcome these challenges. Firstly, the CA (Coordinate Attention) module is incorporated into the backbone network to improve the extraction of key features. Secondly, the improved High-BiFPN is used to enhance YOLOv5s’ neck structure’s capability in fusing multi-scale semantic information. Finally, the improved Better-Ghost Module is employed to reduce the model’s parameters and accelerate the detection speed. We used the CCTSDB2021 dataset to evaluate our model. Compared to YOLOv5s, the proposed Sign-YOLO algorithm in this paper reduces the model parameters by 0.13 M. The precision, recall, F-1 score, and mAP value have improved by 1.02%, 7.01%, 1.84%, and 4.61%, respectively. The FPS value remains around 86 fps. The results show that Sign-YOLO has achieved the optimal balance between accuracy and real-time performance. |
| first_indexed | 2024-03-11T17:17:40Z |
| format | Article |
| id | doaj.art-99573f6394e8417c82cf081b40ec9df6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:40Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-99573f6394e8417c82cf081b40ec9df62023-10-19T23:01:05ZengIEEEIEEE Access2169-35362023-01-011111394111395110.1109/ACCESS.2023.332361810278405Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic SignWeizhen Song0https://orcid.org/0009-0009-7433-4592Shahrel Azmin Suandi1https://orcid.org/0000-0001-9980-7426Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, MalaysiaIntelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, MalaysiaTraffic sign recognition plays a crucial role in the intelligent vehicle’s environment perception system. However, due to varying weather conditions, illumination, and complicated backgrounds, recognizing traffic signs becomes very challenging. A novel lightweight detection model based on YOLOv5s, namely Sign-YOLO, is proposed to overcome these challenges. Firstly, the CA (Coordinate Attention) module is incorporated into the backbone network to improve the extraction of key features. Secondly, the improved High-BiFPN is used to enhance YOLOv5s’ neck structure’s capability in fusing multi-scale semantic information. Finally, the improved Better-Ghost Module is employed to reduce the model’s parameters and accelerate the detection speed. We used the CCTSDB2021 dataset to evaluate our model. Compared to YOLOv5s, the proposed Sign-YOLO algorithm in this paper reduces the model parameters by 0.13 M. The precision, recall, F-1 score, and mAP value have improved by 1.02%, 7.01%, 1.84%, and 4.61%, respectively. The FPS value remains around 86 fps. The results show that Sign-YOLO has achieved the optimal balance between accuracy and real-time performance.https://ieeexplore.ieee.org/document/10278405/Chinese traffic signintelligent vehicledeep learninglightweight modelYOLOv5s |
| spellingShingle | Weizhen Song Shahrel Azmin Suandi Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign IEEE Access Chinese traffic sign intelligent vehicle deep learning lightweight model YOLOv5s |
| title | Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign |
| title_full | Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign |
| title_fullStr | Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign |
| title_full_unstemmed | Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign |
| title_short | Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign |
| title_sort | sign yolo a novel lightweight detection model for chinese traffic sign |
| topic | Chinese traffic sign intelligent vehicle deep learning lightweight model YOLOv5s |
| url | https://ieeexplore.ieee.org/document/10278405/ |
| work_keys_str_mv | AT weizhensong signyoloanovellightweightdetectionmodelforchinesetrafficsign AT shahrelazminsuandi signyoloanovellightweightdetectionmodelforchinesetrafficsign |