关键词: 深度学习；注意力机制；驾驶员行为识别；目标检测算法

Abstract: The paper proposes a distracted driver detection method based on the improved Yolov8. The article designs a weighted bidirectional feature pyramid network (BiFPN) with a small object detection layer and a novel connection structure on the basis of Yolov8. In the backbone network, an efficient multi-scale attention (EMA) module is introduced to enhance the learning and extraction of key information. A new C2f structure is proposed to integrate dynamic convolution and attention mechanisms into C2f. The improved model achieves a 4.6% increase in accuracy while reducing the parameter volume by 27.1%, thus reducing the model size to a certain extent and improving computational efficiency. Finally, generalization experiments on public datasets show that all evaluation metrics are better than those of the original model. Therefore, the research in the article provides a more efficient and accurate solution for distracted driving behavior detection, offering strong support for road safety and traffic regulation.

Key words: Deep learning; Attention mechanism; Driver behavior recognition; Target detection algorithm