| 引用本文: | 张兴旺1,2 ,王凤随1,2 ,杨海燕1,2.基于多特征信息融合的疲劳驾驶检测算法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（4）：62-71 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 针对目前驾驶员疲劳驾驶检测不能兼顾检测速度与检测准确率的问题,提出一种基于 YOLOv7 改进模
型的疲劳驾驶检测算法。 方法 首先,为提高模型的收敛速度,增强模型的检测性能,算法将传统的卷积层替换为深
度过度参数化卷积层,通过增加可学习的参数加快拟合过程;其次,针对存在遮挡目标的场景,传统下采样过程容
易导致特征丢失严重,为提高对遮挡目标检测的准确性,算法引入了基于 SE 注意力改进的 DS-Conv 模块;再次,
为了提高模型对小目标的检测能力,算法在特征提取层中添加了多尺度特征提取 MSS 注意力模块,能够在不同尺
度上捕捉目标的细节和上下文信息;最后,在检测出的人脸上根据 PERCLOS 准则进行疲劳驾驶判定。 结果 实验
结果表明:改进算法在 WIDER FACE 数据集的 Easy、Medium、Hard 子集上分别达到了 96. 0%、94. 6%、88. 1%。 结论
改进算法结构简单,参数量较少,满足人脸目标实时检测的要求,适合部署在车载系统等资源有限的环境中,有效
保障驾驶员的驾驶安全。 |
| 关键词: 疲劳驾驶检测 多特征信息融合 通道注意力 多尺度特征提取 PERCLOS 准则 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Fatigue Driving Detection Algorithm Based on Multi-feature Information Fusion |
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ZHANG Xingwang1 2 WANG Fengsui1 2 YANG Haiyan1 2
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1. School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu 241000 China
2. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Anhui
Wuhu 241000 China
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| Abstract: |
| Objective Due to the current problem that driver fatigue detection cannot simultaneously balance detection
speed and accuracy this paper proposed a fatigue driving detection algorithm based on an improved YOLOv7 model.
Methods Firstly to improve the model?? s convergence speed and enhance its detection performance the traditional
convolutional layer was replaced with a depthwise over-parameterized convolutional layer which accelerated the fitting
process by adding learnable parameters. Secondly in scenes with occluded targets traditional downsampling processes
can lead to significant feature loss. To improve the accuracy of occluded target detection the algorithm introduced a
Depthwise Separable Convolution DS-Conv module based on improved Squeeze-and-Excitation Attention. Thirdly to
enhance the model?? s ability to detect small targets an MSS attention module with multi-scale feature extraction was added
to the feature extraction layer which can capture the details and contextual information of targets at different scales. Finally fatigue driving determination was performed on the detected faces according to the PERCLOS criterion.
Results The experimental results showed that the improved algorithm achieved accuracies of 96. 0% 94. 6% and 88. 1%
on the Easy Medium and Hard subsets of the WIDER FACE dataset respectively. Conclusion The improved
algorithm with its simple structure and small number of parameters is conducive to real-time face target detection and is
suitable for deployment in resource-limited environments such as in-vehicle systems effectively ensuring driver safety. |
| Key words: fatigue driving detection multi-feature information fusion channel attention multi-scale feature extraction
PERCLOS criterion |
