YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments
Traffic target recognition is a crucial technology that has drawn a lot of interest due to the quick development of unmanned and assisted driving systems. However, the precision and performance of target recognition for the more complicated nighttime environment are lower, and the majority of the 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/11080064/ |
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| author | Danyang Zhu Hao Zhou Yunlong Gao Yongjuan Wang |
| author_facet | Danyang Zhu Hao Zhou Yunlong Gao Yongjuan Wang |
| author_sort | Danyang Zhu |
| collection | DOAJ |
| description | Traffic target recognition is a crucial technology that has drawn a lot of interest due to the quick development of unmanned and assisted driving systems. However, the precision and performance of target recognition for the more complicated nighttime environment are lower, and the majority of the present research on traffic target recognition concentrates on the daytime. By using nighttime traffic targets as the research object, this paper suggests YOLOv11-ND, an enhanced target recognition method, to address the aforementioned issues. First, based on WTConv, the WTC3k2 module is intended to take the position of C3k2 in the backbone part. This reduces the amount of model parameters without sacrificing precision. Then, to boost the fusion ability of multi-scale features, the HS-FPN network structure is adopted in the neck section. This improves the detection performance. Lastly, the model is optimized using the Focaler-GIoU loss function to further enhance the detection performance. In comparison to the baseline model YOLOv11, the enhanced model YOLOv11-ND improved the P, R, mAP50, and mAP50-95 measures by 3.1%, 3.4%, 4%, and 3.6%, respectively, according to experimental validation using the FLIR dataset.The method can successfully increase the precision of traffic target detection in urban settings at night, according to the testing results. |
| format | Article |
| id | doaj-art-c990f7188cda4aecb11d88bf2cf2d055 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c990f7188cda4aecb11d88bf2cf2d0552025-08-20T03:55:49ZengIEEEIEEE Access2169-35362025-01-011312448312449310.1109/ACCESS.2025.358901011080064YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban EnvironmentsDanyang Zhu0Hao Zhou1Yunlong Gao2Yongjuan Wang3https://orcid.org/0009-0009-9992-7837School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaHangzhou Zhiyuan Research Ltd., Hangzhou, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaTraffic target recognition is a crucial technology that has drawn a lot of interest due to the quick development of unmanned and assisted driving systems. However, the precision and performance of target recognition for the more complicated nighttime environment are lower, and the majority of the present research on traffic target recognition concentrates on the daytime. By using nighttime traffic targets as the research object, this paper suggests YOLOv11-ND, an enhanced target recognition method, to address the aforementioned issues. First, based on WTConv, the WTC3k2 module is intended to take the position of C3k2 in the backbone part. This reduces the amount of model parameters without sacrificing precision. Then, to boost the fusion ability of multi-scale features, the HS-FPN network structure is adopted in the neck section. This improves the detection performance. Lastly, the model is optimized using the Focaler-GIoU loss function to further enhance the detection performance. In comparison to the baseline model YOLOv11, the enhanced model YOLOv11-ND improved the P, R, mAP50, and mAP50-95 measures by 3.1%, 3.4%, 4%, and 3.6%, respectively, according to experimental validation using the FLIR dataset.The method can successfully increase the precision of traffic target detection in urban settings at night, according to the testing results.https://ieeexplore.ieee.org/document/11080064/FLIRFocaler-GIoUHS-FPNnight target detectionWTC3k2YOLOv11 |
| spellingShingle | Danyang Zhu Hao Zhou Yunlong Gao Yongjuan Wang YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments IEEE Access FLIR Focaler-GIoU HS-FPN night target detection WTC3k2 YOLOv11 |
| title | YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments |
| title_full | YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments |
| title_fullStr | YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments |
| title_full_unstemmed | YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments |
| title_short | YOLOv11-ND: A Method for Identifying Traffic Targets in Nighttime Urban Environments |
| title_sort | yolov11 nd a method for identifying traffic targets in nighttime urban environments |
| topic | FLIR Focaler-GIoU HS-FPN night target detection WTC3k2 YOLOv11 |
| url | https://ieeexplore.ieee.org/document/11080064/ |
| work_keys_str_mv | AT danyangzhu yolov11ndamethodforidentifyingtraffictargetsinnighttimeurbanenvironments AT haozhou yolov11ndamethodforidentifyingtraffictargetsinnighttimeurbanenvironments AT yunlonggao yolov11ndamethodforidentifyingtraffictargetsinnighttimeurbanenvironments AT yongjuanwang yolov11ndamethodforidentifyingtraffictargetsinnighttimeurbanenvironments |