## 6 Conclusions  
This paper introduces an enhanced driver behavior detection model based on YOLOv12, named YOLO-AFR, to address the issue of existing dangerous driving behavior detection models struggling to achieve both accuracy and speed, thereby better tackling the prevalent problem of traffic safety. The model incorporates a Feature-Refinement Feedback Network (FRFN) to reconstruct the original A2C2f module in YOLOv12, forming a novel A2C2f-FRFN structure that enables adaptive optimization of multiscale features and significantly improves feature extraction in complex in-vehicle environments. A new C3k2_SC-Conv module is proposed, which integrates self-calibrated convolution (SC-Conv) into the C3k2 structure to extend the receptive field and elevate the accuracy of driver behavior detection. Additionally, the SEAM module is deployed in the detection head to boost global contextual awareness and prediction accuracy, effectively reducing missed and false detections in scenarios involving occlusions inside the vehicle. Experimental results demonstrate that YOLO-AFR contains only 2.42 million parameters and 5.7 GFLOPs, achieving 0.976 and 0.989 [EMAIL_ADDRESS] on the YawDD-E and SfdDD datasets, respectively. This represents an absolute improvement of 1.3% and 1.8% compared to the baseline model YOLOv12n (0.963 and 0.971). It also outperforms recent models such as YOLO-SGC (0.972 and 0.980), YOLOv10n (0.960 and 0.966), and YOLOv11n (0.963 and 0.972). This confirms the model's effectiveness in accurately detecting dangerous driving behaviors in complex environments. Furthermore, the model maintains real-time performance, with a processing speed of 104.59 FPS, significantly higher than recent baseline models and slightly lower than YOLO11n's 123.21 FPS. Future work will concentrate on several specific avenues. Firstly, to further enhance detection accuracy for challenging categories, particularly those involving high similarity to normal states or very small objects (e.g., \"Single hand\" driving, \"Playing phone\"), we will continue to refine attention mechanisms, optimize multiscale feature fusion strategies, investigate advanced loss functions, and adapt training methodologies. Secondly, to significantly improve computational efficiency for deployment on resource-constrained in-vehicle platforms, we will explore techniques such as quantization-aware training and network pruning to reduce model size and accelerate inference speed beyond the current 104.59 FPS. Thirdly, we will continue to research the deployment of the model in embedded systems to study its capabilities under performance constraints. Finally, a key direction will be the practical integration of the enhanced YOLO-AFR model into advanced driver assistance systems (ADAS). This will involve researching driver dangerous behavior recognition algorithms, such as by identifying combined behaviors, to better apply the object detection model in the field of practical traffic safety, thereby contributing more directly to on-road safety. Conceptualization, T.G. and B.N. ; methodology, T.G. ; software, T.G. ; validation, T.G., B.N. and Y.X. ; formal analysis, T.G. ; investigation, T.G. ; resources, T.G. ; data curation, T.G. ; writing--original draft preparation, T.G. ; writing--review and editing, B.N. ; visualization, T.G. ; supervision, Y.X. ; project administration, Y.X. ; funding acquisition, B.N. All authors have read and agreed to the published version of the manuscript. This study received partial support from the National Natural Science Foundation of China (Grant No. 61976032) and the High-Performance Computing Center at Dalian Maritime University. Not applicable. The data presented in this study are available on request from the corresponding author. We would like to sincerely thank all the members of the Artificial Intelligence Department of Safewind Student Association at Dalian Maritime University for their valuable contributions to this study. We would also like to thank all our colleagues who supported this work with their expertise and dedication.