YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection - Tianchen Ge; Bo Ning; Yiwu Xie (Applied Sciences)
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.