桂林电子科技大学学报2025，Vol.45Issue(1)：69-75,7.DOI:10.16725/j.1673-808X.202224
改进LBP和CNN相结合的疲劳驾驶检测方法
A combined algorithm of improved LBP and CNN for driver fatigue detection
摘要
Abstract
A chunked LBP feature texture extraction method based on increasing weights of facial regions is proposed to address the problem of incomplete or partial loss of facial fatigue information resulting in inadequate characterisation of the driver's fatigue state,leading to low accuracy of fatigue detection.A basic image dataset for fatigue recognition is constructed based on driver images un-der different lighting conditions,and the acquisition and generalisation of sample images in the dataset is completed through self-built datasets,pre-processing and data enhancement work.An 8×8 block-weighted LBP algorithm is proposed and used to extract the driver's facial feature texture from the dataset images,which is used as the input of the convolutional neural network for model learn-ing and training.The experimental results show that the proposed algorithm is fast in feature extraction,taking only 0.01s,and the fatigue detection model has good recognition accuracy and generalisation ability,with an accuracy rate of 93.52%.The proposed al-gorithm not only retains the ability to characterise image texture changes,but also effectively reduces feature redundancy,providing a feasible method for the recognition of driver fatigue states.关键词
事故预防/疲劳驾驶识别/面部特征权重/局部二值模式(LBP)/卷积神经网络(CNN)Key words
accident prevention/driver fatigue recognition/the weight of facial features/local binary patterns(LBP)/convolutional neural network(CNN)分类
信息技术与安全科学引用本文复制引用
黄燕卿,英红..改进LBP和CNN相结合的疲劳驾驶检测方法[J].桂林电子科技大学学报,2025,45(1):69-75,7.基金项目
国家自然科学基金(51968011,51668012) （51968011,51668012）
桂林电子科技大学研究生创新计划(2021YCXS177) （2021YCXS177）
