Attention-Guided Lightweight MobileNetV2 for Real-Time Driver Drowsiness Classification on Edge-IoT Systems

Abstract

Driver drowsiness is a major cause of traffic accidents, so Edge-IoT platforms with limited resources need to be able to accurately and quickly detect when drivers are drowsy. This study examines attention-guided lightweight CNN design predicated on MobileNetV2 for real-time driver drowsiness detection. The authors compare a SE-enhanced MobileNetV2 to the baseline model and a structurally optimized version that uses Depthwise Separable Convolution (DSC), Bottleneck blocks, and Expansion layers. Experiments on 500 images demonstrate that channel attention enhances feature discrimination, whereas structural optimization yields the most resilient trade-off between accuracy and latency. Statistical validation employing 95% confidence intervals and two-proportion Z-tests substantiates the significance of these enhancements. The proposed models support real-time inference despite their small size (about 2.6 million parameters and 315 million FLOPs). These findings suggest structural optimization is more important than attention mechanisms in designing lightweight CNNs for embedded driver monitoring.