ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving
Abstract Drawing inspiration from the state‐of‐the‐art object detection framework YOLOv8, a new model termed adverse weather net (ADWNet) is proposed. To enhance the model's feature extraction capabilities, the efficient multi‐scale attention (EMA) module has been integrated into the backbone....
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-10-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12566 |
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| _version_ | 1850265494844604416 |
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| author | Xinyun Feng Tao Peng Ningguo Qiao Haitao Li Qiang Chen Rui Zhang Tingting Duan JinFeng Gong |
| author_facet | Xinyun Feng Tao Peng Ningguo Qiao Haitao Li Qiang Chen Rui Zhang Tingting Duan JinFeng Gong |
| author_sort | Xinyun Feng |
| collection | DOAJ |
| description | Abstract Drawing inspiration from the state‐of‐the‐art object detection framework YOLOv8, a new model termed adverse weather net (ADWNet) is proposed. To enhance the model's feature extraction capabilities, the efficient multi‐scale attention (EMA) module has been integrated into the backbone. To address the problem of information loss in fused features, Neck has been replaced with RepGDNeck. Simultaneously, to expedite the model's convergence, the bounding box's loss function has been optimized to SIoU loss. To elucidate the advantages of ADWNet in the context of adverse weather conditions, ablation studies and comparative experiments were conducted. The results indicate that although the model's parameter count increased by 18.4%, the accuracy for detecting rain, snow, and fog in adverse weather conditions improved by 22%, while the FLOPs (floating point operations) decreased by 5%. The results of the comparison experiments conducted on the WEDGE dataset show that ADWNet outperforms other object detection models in adverse weather in terms of accuracy, model parameters and FLOPs. To validate ADWNet's real‐world efficacy, data was extracted from a car recorder under adverse conditions on highways, visual inference was conducted, and its accuracy was demonstrated in interpreting real‐world scenarios. The config files are available at https://github.com/Xinyun‐Feng/ADWNet. |
| format | Article |
| id | doaj-art-92d7a58ce1774fc9b2d8d421efcfb20a |
| institution | OA Journals |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-92d7a58ce1774fc9b2d8d421efcfb20a2025-08-20T01:54:25ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-10-0118101962197910.1049/itr2.12566ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous drivingXinyun Feng0Tao Peng1Ningguo Qiao2Haitao Li3Qiang Chen4Rui Zhang5Tingting Duan6JinFeng Gong7College of Automobile and TransportationTianjin University of Technology and EducationTianjinChinaCollege of Automobile and TransportationTianjin University of Technology and EducationTianjinChinaCollege of Automobile and TransportationTianjin University of Technology and EducationTianjinChinaCollege of Automobile and TransportationTianjin University of Technology and EducationTianjinChinaCollege of Automobile and TransportationTianjin University of Technology and EducationTianjinChinaCollege of Automobile and TransportationTianjin University of Technology and EducationTianjinChinaAutomobile and Rail Transportation CollegeTianjin Sino‐German University of Applied SciencesTianjinChinaChina Automotive Technology and Research Center Co., LtdTianjinChinaAbstract Drawing inspiration from the state‐of‐the‐art object detection framework YOLOv8, a new model termed adverse weather net (ADWNet) is proposed. To enhance the model's feature extraction capabilities, the efficient multi‐scale attention (EMA) module has been integrated into the backbone. To address the problem of information loss in fused features, Neck has been replaced with RepGDNeck. Simultaneously, to expedite the model's convergence, the bounding box's loss function has been optimized to SIoU loss. To elucidate the advantages of ADWNet in the context of adverse weather conditions, ablation studies and comparative experiments were conducted. The results indicate that although the model's parameter count increased by 18.4%, the accuracy for detecting rain, snow, and fog in adverse weather conditions improved by 22%, while the FLOPs (floating point operations) decreased by 5%. The results of the comparison experiments conducted on the WEDGE dataset show that ADWNet outperforms other object detection models in adverse weather in terms of accuracy, model parameters and FLOPs. To validate ADWNet's real‐world efficacy, data was extracted from a car recorder under adverse conditions on highways, visual inference was conducted, and its accuracy was demonstrated in interpreting real‐world scenarios. The config files are available at https://github.com/Xinyun‐Feng/ADWNet.https://doi.org/10.1049/itr2.12566artificial intelligenceautomobilesautonomous driving |
| spellingShingle | Xinyun Feng Tao Peng Ningguo Qiao Haitao Li Qiang Chen Rui Zhang Tingting Duan JinFeng Gong ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving IET Intelligent Transport Systems artificial intelligence automobiles autonomous driving |
| title | ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving |
| title_full | ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving |
| title_fullStr | ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving |
| title_full_unstemmed | ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving |
| title_short | ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving |
| title_sort | adwnet an improved detector based on yolov8 for application in adverse weather for autonomous driving |
| topic | artificial intelligence automobiles autonomous driving |
| url | https://doi.org/10.1049/itr2.12566 |
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