With the development of deep learning technology, the detection and classification of distracted driving behaviour requires higher accuracy. Existing deep learning-based methods are computationally intensive and parameter redundant, limiting the efficiency and accuracy in practical applications. To solve this problem, this study proposes an improved YOLOv8 detection method based on the original YOLOv8 model by integrating the BoTNet module, GAM attention mechanism and EIoU loss function. By optimising the feature extraction and multi-scale feature fusion strategies, the training and inference processes are simplified, and the detection accuracy and efficiency are significantly improved. Experimental results show that the improved model performs well in both detection speed and accuracy, with an accuracy rate of 99.4%, and the model is smaller and easy to deploy, which is able to identify and classify distracted driving behaviours in real time, provide timely warnings, and enhance driving safety.

翻译：随着深度学习技术的发展，驾驶分心行为的检测与分类对准确性提出了更高要求。现有基于深度学习的方法存在计算量大、参数冗余等问题，限制了实际应用中的效率与精度。为解决该问题，本研究在原始YOLOv8模型基础上，通过集成BoTNet模块、GAM注意力机制与EIoU损失函数，提出一种改进的YOLOv8检测方法。通过优化特征提取与多尺度特征融合策略，简化了训练与推理过程，显著提升了检测精度与效率。实验结果表明，改进模型在检测速度与精度上均表现优异，准确率达到99.4%，且模型体积更小、易于部署，能够实时识别与分类驾驶分心行为，及时发出预警，有效提升驾驶安全性。