Cross-Driver Distracted Driving Behavior Recognition with Directional Enhancement and Exponential Weight Smoothing

Driver distraction recognition is important for in-cabin driver monitoring, but random image splits can overestimate performance because driver identity, in-vehicle background, and scene cues may be shared between training and evaluation data. To improve recognition under the executed driver-disjoint evaluation, this paper proposes Directional Enhancement and Weight Smoothing EfficientNet (DEWS-EfficientNet), a single-frame classification framework built on EfficientNet-B0. The method integrates label-consistent flipping (LCF), a directional enhancement (DE) Block, and exponential weight smoothing (EWS). LCF swaps left- and right-hand phone/texting labels after horizontal flipping. The DE Block uses 3 × 3 depthwise and 1 × 5/5 × 1 asymmetric depthwise convolutions to model local actions and directional patterns. EWS uses smoothed weights for validation and testing without adding an inference branch. Experiments are conducted on AUC Distracted Driver V2 (AUC2) and State Farm Distracted Driver Detection (SFD3) under driver-disjoint splits. DEWS-EfficientNet achieves the best accuracies of 82.39% and 89.00% on AUC2 and SFD3, improving the EfficientNet-B0 baseline by 11.83 and 10.04 percentage points, respectively. Ablation and visualization results further indicate that the proposed components improve recognition performance under the executed driver-disjoint splits while preserving a compact classification pipeline.