改进YOLO11的轻量化驾驶员疲劳检测算法OA

To address the challenges posed by large number of parameters and high computational re-quirements of driver fatigue detection models,which make deployment on mobile devices difficult,a lightweight deep learning-based driver fatigue detection model,YOLO11-SFL,is proposed.The model introduces StarNet as the backbone network to simplify the overall network structure,thereby reducing the total number of parameters and computational costs while improving operational efficiency.A FasterNet Block is adopted to enhance the C3k2,optimizing its convolutional structure to eliminate redundant com-putations.Furthermore,a Lightweight Asymmetric Detection Head(LADH)with multi-level compression techniques is employed to improve the model's adaptability to detect objects of various sizes.This ap-proach reduces the number of model parameters while compensating for some of the accuracy loss associ-ated with lightweight models.Experimental results indicate that the proposed fatigue detection model,YOLO11-SFL,maintains high accuracy while significantly reducing its parameter count,computational load,and model size to 57.7%,57.1%,and 60.2%of those of the original YOLO11 model,respec-tively.Comparisons with other lightweight object detection models validate the effectiveness and superiority of this method in balancing lightweight design and performance.