YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection
Accurate detection of dangerous driving behaviors is crucial for improving the safety of intelligent transportation systems. However, existing methods often struggle with limited feature extraction capabilities and insufficient attention to multiscale and contextual information. To overcome these li...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6090 |
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| author | Tianchen Ge Bo Ning Yiwu Xie |
| author_facet | Tianchen Ge Bo Ning Yiwu Xie |
| author_sort | Tianchen Ge |
| collection | DOAJ |
| description | Accurate detection of dangerous driving behaviors is crucial for improving the safety of intelligent transportation systems. However, existing methods often struggle with limited feature extraction capabilities and insufficient attention to multiscale and contextual information. To overcome these limitations, we propose YOLO-AFR (YOLO with Adaptive Feature Refinement) for dangerous driving behavior detection. YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. These three modules combine to form a Calibration-Refinement Loop, which progressively reduces redundancy and enhances discriminative features layer by layer. We evaluate YOLO-AFR on two public driver behavior datasets, YawDD-E and SfdDD. Experimental results show that YOLO-AFR significantly outperforms the baseline YOLOv12 model, achieving improvements of 1.3% and 1.8% in mAP@0.5, and 2.6% and 12.3% in mAP@0.5:0.95 on the YawDD-E and SfdDD datasets, respectively, demonstrating its superior performance in complex driving scenarios while maintaining high inference speed. |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d2fe72417ea949df8af1e3b0ecabae9d2025-08-20T02:33:01ZengMDPI AGApplied Sciences2076-34172025-05-011511609010.3390/app15116090YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior DetectionTianchen Ge0Bo Ning1Yiwu Xie2Information Science and Technology College, Dalian Maritime University, Dalian 116026, ChinaInformation Science and Technology College, Dalian Maritime University, Dalian 116026, ChinaInformation Science and Technology College, Dalian Maritime University, Dalian 116026, ChinaAccurate detection of dangerous driving behaviors is crucial for improving the safety of intelligent transportation systems. However, existing methods often struggle with limited feature extraction capabilities and insufficient attention to multiscale and contextual information. To overcome these limitations, we propose YOLO-AFR (YOLO with Adaptive Feature Refinement) for dangerous driving behavior detection. YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. These three modules combine to form a Calibration-Refinement Loop, which progressively reduces redundancy and enhances discriminative features layer by layer. We evaluate YOLO-AFR on two public driver behavior datasets, YawDD-E and SfdDD. Experimental results show that YOLO-AFR significantly outperforms the baseline YOLOv12 model, achieving improvements of 1.3% and 1.8% in mAP@0.5, and 2.6% and 12.3% in mAP@0.5:0.95 on the YawDD-E and SfdDD datasets, respectively, demonstrating its superior performance in complex driving scenarios while maintaining high inference speed.https://www.mdpi.com/2076-3417/15/11/6090dangerous driving behavior detectionYOLOfeature refinementself-calibrated convolutionseparated and enhancement attentiondeep learning |
| spellingShingle | Tianchen Ge Bo Ning Yiwu Xie YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection Applied Sciences dangerous driving behavior detection YOLO feature refinement self-calibrated convolution separated and enhancement attention deep learning |
| title | YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection |
| title_full | YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection |
| title_fullStr | YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection |
| title_full_unstemmed | YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection |
| title_short | YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection |
| title_sort | yolo afr an improved yolov12 based model for accurate and real time dangerous driving behavior detection |
| topic | dangerous driving behavior detection YOLO feature refinement self-calibrated convolution separated and enhancement attention deep learning |
| url | https://www.mdpi.com/2076-3417/15/11/6090 |
| work_keys_str_mv | AT tianchenge yoloafranimprovedyolov12basedmodelforaccurateandrealtimedangerousdrivingbehaviordetection AT boning yoloafranimprovedyolov12basedmodelforaccurateandrealtimedangerousdrivingbehaviordetection AT yiwuxie yoloafranimprovedyolov12basedmodelforaccurateandrealtimedangerousdrivingbehaviordetection |