兵工自动化2024，Vol.43Issue(12)：26-29,41,5.DOI:10.7690/bgzdh.2024.12.007
基于RepVGG的疲劳驾驶检测算法
Fatigue Driving Detection Algorithm Based on RepVGG
摘要
Abstract
In order to improve the accuracy and deployability of fatigue driving detection method,a fatigue driving detection algorithm based on RepVGG is proposed.An atrous spatial pyramid pooling(ASPP)module was added to the model to capture the multi-scale fatigue characteristics.A Convolutional block attention module(convolutional block attention module,CBAM)is combined with an ASPP module and separately applied to the model to further emphasize and capture the multi-scale information and important regional information expressed by fatigue features,and to suppress the background information in the image.Thereby improving the performance and robustness of the model.The results show that the accuracy of the improved RepVGG algorithm on the fatigue driving data set reaches 97.34%,which is 2.51%higher than that of the original algorithm,and the number of model parameters is only 7.1×106,which has good detection accuracy and deployability.关键词
RepVGG/疲劳驾驶检测/ASPP/CBAMKey words
RepVGG/fatigue driving detection/ASPP/CBAM分类
信息技术与安全科学引用本文复制引用
夏庆锋,李明阳,宋志强,许可儿..基于RepVGG的疲劳驾驶检测算法[J].兵工自动化,2024,43(12):26-29,41,5.基金项目
江苏省产学研合作项目(BY20230688) （BY20230688）
新一代信息技术创新项目(2022IT208) （2022IT208）
江苏高校"青蓝工程" （）
