A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition
The widespread use of electric bikes has made rider safety a crucial issue in intelligent transportation research. To address the limitations of existing methods in terms of accuracy and robustness in electric bike license plate recognition, helmet detection, and rider detection tasks, this paper pr...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11079553/ |
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| author | Shaohui Zhong Xiaofei Liu |
| author_facet | Shaohui Zhong Xiaofei Liu |
| author_sort | Shaohui Zhong |
| collection | DOAJ |
| description | The widespread use of electric bikes has made rider safety a crucial issue in intelligent transportation research. To address the limitations of existing methods in terms of accuracy and robustness in electric bike license plate recognition, helmet detection, and rider detection tasks, this paper proposes a novel electric bike safety detection method, F-YOLOv8, which integrates license plate region recognition. Based on the YOLOv8 architecture, this method incorporates a feature pyramid network (FPN) to optimize multi-scale object detection capabilities, while introducing an attention mechanism to enhance the extraction of key features. The study constructs a high-quality dataset covering various riding scenarios and violations, including image data collected in Guangzhou and Foshan, Guangdong Province, annotated using LabelImg.The label types include Ebike (rider), plate (license plate), gzplace (license plate region), helmet (wearing a helmet), nohelmet (not wearing a helmet), and manned (carrying a passenger). Experimental results demonstrate significant improvements across all metrics: the precision on Ebike reached 96.1%, on Plate reached 92.9%, on Helmet reached 84.6%, and on Manned tasks reached 67.9%. This study provides an efficient and reliable detection method for enhancing electric bike riding safety management, laying a technical foundation for the development of intelligent transportation systems. |
| format | Article |
| id | doaj-art-60edb7436cea487f8539eb28a70f0d9f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-60edb7436cea487f8539eb28a70f0d9f2025-08-20T03:31:49ZengIEEEIEEE Access2169-35362025-01-011312455612456810.1109/ACCESS.2025.358850611079553A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region RecognitionShaohui Zhong0https://orcid.org/0009-0006-4780-5959Xiaofei Liu1https://orcid.org/0009-0004-1207-2455Software College, Changsha Institute of Technology, Changsha, Hunan, ChinaSchool of Artificial Intelligence, Guangdong Open University, Guangzhou, Guangdong, ChinaThe widespread use of electric bikes has made rider safety a crucial issue in intelligent transportation research. To address the limitations of existing methods in terms of accuracy and robustness in electric bike license plate recognition, helmet detection, and rider detection tasks, this paper proposes a novel electric bike safety detection method, F-YOLOv8, which integrates license plate region recognition. Based on the YOLOv8 architecture, this method incorporates a feature pyramid network (FPN) to optimize multi-scale object detection capabilities, while introducing an attention mechanism to enhance the extraction of key features. The study constructs a high-quality dataset covering various riding scenarios and violations, including image data collected in Guangzhou and Foshan, Guangdong Province, annotated using LabelImg.The label types include Ebike (rider), plate (license plate), gzplace (license plate region), helmet (wearing a helmet), nohelmet (not wearing a helmet), and manned (carrying a passenger). Experimental results demonstrate significant improvements across all metrics: the precision on Ebike reached 96.1%, on Plate reached 92.9%, on Helmet reached 84.6%, and on Manned tasks reached 67.9%. This study provides an efficient and reliable detection method for enhancing electric bike riding safety management, laying a technical foundation for the development of intelligent transportation systems.https://ieeexplore.ieee.org/document/11079553/Electric bike safety detectionlicense plate recognitionhelmet detectionrider detectionYOLO |
| spellingShingle | Shaohui Zhong Xiaofei Liu A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition IEEE Access Electric bike safety detection license plate recognition helmet detection rider detection YOLO |
| title | A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition |
| title_full | A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition |
| title_fullStr | A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition |
| title_full_unstemmed | A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition |
| title_short | A Safety Detection Method for Electric Bike Riding Incorporating License Plate Region Recognition |
| title_sort | safety detection method for electric bike riding incorporating license plate region recognition |
| topic | Electric bike safety detection license plate recognition helmet detection rider detection YOLO |
| url | https://ieeexplore.ieee.org/document/11079553/ |
| work_keys_str_mv | AT shaohuizhong asafetydetectionmethodforelectricbikeridingincorporatinglicenseplateregionrecognition AT xiaofeiliu asafetydetectionmethodforelectricbikeridingincorporatinglicenseplateregionrecognition AT shaohuizhong safetydetectionmethodforelectricbikeridingincorporatinglicenseplateregionrecognition AT xiaofeiliu safetydetectionmethodforelectricbikeridingincorporatinglicenseplateregionrecognition |