人脸特征识别驱动的汽车疲劳驾驶预警系统研究

作     者：祥智扬 许瑞瑞 潘磊 李霞 张安好 

作者机构：桂林电子科技大学信息与通信学院广西 桂林 桂林电子科技大学计算机科学与工程学院广西 桂林 广西科技大学第一附属医院广西 柳州 

基　　金：国家级桂林电子科技大学大学生创新创业训练计划项目资助 项目编号:202410595119X 

出 版 物：《计算机科学与应用》 (Computer Science and Application)

年 卷 期：2025年第15卷第1期

页      码：100-107页

摘      要：针对汽车疲劳驾驶导致交通事故频发的问题,设计实现了一种基于人脸特征识别的汽车疲劳驾驶预警系统。该系统首先通过摄像头采集驾驶员面部图像数据,再利用深度学习中的卷积神经网络进行人脸特征提取,获取包含眨眼频率、打哈欠次数、头部姿态等关键特征参数。在此基础上,建立了多维度的疲劳评估模型,该模型通过分析连续视频帧中面部特征的动态变化规律,实现了对驾驶疲劳状态的实时监测和预警。为提高系统性能,对传统卷积神经网络结构进行了优化,增加了注意力机制模块,同时采用了长短时记忆网络(LSTM)来捕捉驾驶员面部特征的时序变化特性。实验结果表明,该系统在不同光照和驾驶环境下的疲劳检测准确率达到95.3%,平均响应时间低于0.5秒,具有较强的实用性和可靠性,能够有效降低疲劳驾驶带来的安全风险。In response to the problem of frequent traffic accidents caused by car fatigue driving, a car fatigue driving warning system based on facial feature recognition has been designed and implemented. The system first collects facial image data of the driver through a camera, and then uses convolutional neural networks in deep learning to extract facial features, including key feature parameters such as blink frequency, yawning frequency, and head posture. On this basis, a multidimensional fatigue assessment model was established, which achieved real-time monitoring and warning of driving fatigue status by analyzing the dynamic changes of facial features in continuous video frames. To improve system performance, the traditional convolutional neural network structure was optimized by adding an attention mechanism module, and a long short-term memory network (LSTM) was used to capture the temporal variation characteristics of driver facial features. The experimental results show that the fatigue detection accuracy of the system reaches 95.3% under different lighting and driving environments, with an average response time of less than 0.5 seconds. It has strong practicality and reliability, and can effectively reduce the safety risks caused by fatigue driving.

主 题 词：人脸特征识别 疲劳驾驶 预警系统 深度学习 

学科分类：08[工学] 0812[工学-测绘类] 

D　O　I：10.12677/csa.2025.151010

馆 藏 号：203156989...