A Lightweight and Efficient Approach for Distracted Driving Detection Based on YOLOv8

To overcome the issues of excessive computation and resource usage in distracted driving detection systems, this study introduces a compact detection framework named YOLOv8s-FPNE, built upon the YOLOv8 architecture. The proposed model incorporates FasterNet, Partial Convolution (PConv) layers, a Normalized Attention Mechanism (NAM), and the Focal-EIoU loss to achieve an optimal trade-off between accuracy and efficiency. FasterNet together with PConv enhances feature extraction while reducing redundancy, NAM strengthens the model’s sensitivity to key spatial and channel information, and Focal-EIoU refines bounding-box regression, particularly for hard-to-detect samples. Experimental evaluations on a public distracted driving dataset show that YOLOv8s-FPNE reduces the number of parameters by 21.7% and computational cost (FLOPS) by 23.6% relative to the original YOLOv8s, attaining an [email protected] of 81.6%, which surpasses existing lightweight detection methods. Ablation analyses verify the contribution of each component, and comparative studies further confirm the advantages of NAM and Focal-EIoU. The results demonstrate that the proposed method provides a practical and efficient solution for real-time distracted driving detection on embedded and resource-limited platforms.