Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

Wang, Yi; Wang, Liguan

doi:10.3390/math9222908

Cited by 4 publications

(2 citation statements)

References 21 publications

(18 reference statements)

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“…Traditional driver fatigue detection methods encounter limitations in capturing the nuanced spatiotemporal dynamics of continuous video frames, crucial for identifying subtle fatigue-related facial movements [ 22 , 23 ]. Addressing these deficiencies, we introduce the Composite Action Recognition Network, merging the capabilities of 3D convolutional networks (3D CNN) and Long Short-Term Memory networks (LSTM).…”

Section: Our Approachmentioning

confidence: 99%

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Lin,

Zuo

2024

PLoS ONE

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Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method’s adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.

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Section: Our Approachmentioning

confidence: 99%

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Lin,

Zuo

2024

PLoS ONE

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“…In other words, convolutional layers are used to extract spatial features from the frames, and the spatial features are sent to LSTM layers at each time step to model temporal sequences, as shown in Figure 5. In this way, the network learns spatial and temporal features immediately in an end-to-end training process, which makes the model more stable [22][23][24][25][26]. This means that most of the time ϕ_v (v_t), the convolutional inference, and training can be completed in parallel over time.…”

Section: Long-term Recurrent Convolutional Modelmentioning

confidence: 99%

Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders

Alkahtani,

Ahmed,

Aldhyani

et al. 2023

Mathematics

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Autism spectrum disorder (ASD), or autism, can be diagnosed based on a lack of behavioral skills and social communication. The most prominent method of diagnosing ASD in children is observing the child’s behavior, including some of the signs that the child repeats. Hand flapping is a common stimming behavior in children with ASD. This research paper aims to identify children’s abnormal behavior, which might be a sign of autism, using videos recorded in a natural setting during the children’s regular activities. Specifically, this study seeks to classify self-stimulatory activities, such as hand flapping, as well as normal behavior in real-time. Two deep learning video classification methods are used to be trained on the publicly available Self-Stimulatory Behavior Dataset (SSBD). The first method is VGG-16-LSTM; VGG-16 to spatial feature extraction and long short-term memory networks (LSTM) for temporal features. The second method is a long-term recurrent convolutional network (LRCN) that learns spatial and temporal features immediately in end-to-end training. The VGG-16-LSTM achieved 0.93% on the testing set, while the LRCN model achieved an accuracy of 0.96% on the testing set.

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Self-Supervised Multi-Granularity Graph Attention Network for Vision-Based Driver Fatigue Detection

Huang,

Liu,

Chang

et al. 2024

IEEE Trans. Emerg. Top. Comput. Intell.

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Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

Cited by 4 publications

References 21 publications

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Deep Learning Algorithms for Behavioral Analysis in Diagnosing Neurodevelopmental Disorders

Self-Supervised Multi-Granularity Graph Attention Network for Vision-Based Driver Fatigue Detection

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