A dense multi-pooling convolutional network for driving fatigue detection
Abstract Driver fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles, who are more susceptible to fatigue due to prolonged driving hours and monotonous conditions during their journeys. Existing vision-based driver fatigue detection methods fail to accu...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-99441-7 |
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| Summary: | Abstract Driver fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles, who are more susceptible to fatigue due to prolonged driving hours and monotonous conditions during their journeys. Existing vision-based driver fatigue detection methods fail to accurately and timely judge fatigue in complex driving scenarios (e.g., when wearing glasses or in the presence of non-driver individuals). To address these issues, this paper proposes a driving fatigue detection method based on a novel network and the analysis of driver facial actions. The proposed approach mainly consists of three submodules, i.e. Driver’s State Detection (DSD), Dense Multi-Pooling Convolutional Network (DMP-Net), and Driving Fatigue Detection (DFD). In the DSD module, MTCNN is employed to locate the driver’s face and detect facial landmarks in real time. Additionally, a face detection bounding box filtering algorithm is proposed to reduce false detections of the driver. To accurately detect the states of the driver’s facial actions, we propose the DMP-Net network, which contains only a small number of parameters and outperforms existing methods in terms of accuracy and time consumption. The DFD module determines whether the driver is fatigued by comparing a reasonable threshold with the frequency of mouth opening (FM) and the percentage of eyelid closure over the pupil over time parameter (PERCLOS). Results of the experiments based on benchmarks and our self-collected datasets show that our method achieves 99.25% accuracy on the CEW dataset, 99.24% accuracy on the ZJU dataset, and 99.12% accuracy on our self-collected dataset. Our proposed driving fatigue detection method has as a high accuracy in real time and outperforms the existing methods. |
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| ISSN: | 2045-2322 |