关键词: 驾驶员监控系统；卷积神经网络；YOLOv5；MobileNet

Abstract: With the rapid advancement of intelligent driving technologies, the driver monitoring system (DMS) serves as a core module for road traffic safety guarantee and attracts growing industry attention. This paper constructs a driver monitoring system algorithm based on convolutional neural network (CNN) to improve driving safety and intelligence of commercial vehicles. The system consists of two major modules: cockpit monitoring and facial monitoring. The cockpit monitoring module adopts the YOLOv5 network structure. It learns driver behaviors such as sitting postures and hand movements through labeled data to identify hazardous driving behaviors including phone use and leaving the driving position. The facial monitoring module also applies the YOLOv5 network structure for training on self-built datasets. It automatically recognizes drivers' facial expressions and head postures to assess drivers' fatigue levels and attention status. Meanwhile, the system builds a classification detection framework based on MobileNet networks to distinguish camera occluded and non-occluded states. The YOLOv5n convolutional neural network integrated with Ghost modules cuts system parameters by 85%. It balances model lightweight design and feature recognition efficiency, maintains stable recognition accuracy under various working conditions, delivers favorable accuracy and real-time performance, and enhances the active safety monitoring capacity for drivers.

Key words: driver monitoring system; convolutional neural networks; YOLOv5; MobileNet