RepViT Based Lightweight Architecture for Distracted Driving Detection

Distracted driving is a significant cause of traffic accidents and has becoming an increasingly severe road safety concern, in particular with driver distractions such as using phones, talking, and makeup applications. Therefore, real-time detection of driver behavior and reactions during driving is crucial for improving traffic safety. To resolve the problems of large parameter counts and inaccurate classification in existing distracted driving detection models, this paper presents a lightweight model, named RepViT-MCFM, which features low latency and robustly detects driver distraction. Specifically, we designed a multi-scale cross-fusion denoising module (MCFM) based on Haar wavelet transform, utilized the RepViT module improved from MobileNetV3 as the backbone feature extractor, and designed the RepViT-MCFM model with a low-latency simple classifier. We have conducted quantitative and qualitative evaluations on the State-Farm dataset, extensive experiments demonstrate that the designed RepViT model, compared to existing CNN-based detection models, maintains high accuracy while achieving lower latency and fewer parameters.