针对现有分心驾驶检测模型计算复杂度高、检测精度有限的问题，提出了一种基于YOLOv8n的轻量化分心驾驶检测算法。首先，采用RevCol_faster网络重构YOLOv8n主干，通过可逆列结构增强特征提取能力，同时减少参数量与计算量。其次，结合C2f_Star模块优化颈部结构，提升多尺度特征融合能力，以提高对不同大小、不同距离目标的检测准确性。此外，采用LSCD（lightweight shared convolution detection）共享卷积检测头，减少计算冗余，提高检测速度。最后，利用EIoU损失函数替代原有的CIoU，以增强目标框的形状感知能力，提高定位精度。实验结果表明，改进后的模型在Kaggle驾驶员行为数据集上实现了96.8%的mAP50，参数量降低至1.3×10⁶，计算量减少至3.0×10⁹，相比YOLOv8n分别减少57.60%和56.67%，且推理速度提升4.5%。该研究为资源受限环境下的分心驾驶检测提供了一种高效、低计算成本的解决方案。

Aiming at the problems of high computational complexity and limited detection accuracy of existing distracted driving detection models, a lightweight distracted driving detection algorithm based on YOLOv8n was proposed. Firstly, the RevCol_faster network was used to reconstruct the YOLOv8n backbone, and the feature extraction ability was enhanced through the reversible column structure, while reducing the number of parameters and calculation. Secondly, the C2f_Star module was combined to optimize the neck structure and improve the multi-scale feature fusion ability to improve the detection accuracy of targets of different sizes and different distances. In addition, LSCD(lightweight shared convolution detection) was used to share the convolution detection head to reduce the calculation redundancy and improve the detection speed. Finally, the EIoU loss function was used to replace the original CIoU to enhance the shape perception of the target box and improve the positioning accuracy. Experimental results show that the improved model achieves 96.8% mAP50 on the Kaggle driver behavior dataset, reduces the number of parameters to 1.3 × 10⁶, reduces the amount of calculation to 3.0 × 10⁹, and reduces 57.60% and 56.67% respectively compared with YOLOv8n, and improves the inference speed by 4.5%. This study provides an efficient and computationally inexpensive solution for distracted driving detection in resource-constrained environments.