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Low-light driver drowsiness detection for real-time safety assistance using dual attention mechanisms in deep learning model.

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Τίτλος: Low-light driver drowsiness detection for real-time safety assistance using dual attention mechanisms in deep learning model.
Συγγραφείς: Saxena S; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India., Angel; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India., Khurana M; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India., Tiwari S; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India., Shakya HK; Department of Artificial Intelligence and Machine Learning, School of Computer Science & Engineering, Manipal University Jaipur, Jaipur, India. harish.shakya@jaipur.manipal.edu.
Πηγή: Scientific reports [Sci Rep] 2026 Apr 20; Vol. 16 (1). Date of Electronic Publication: 2026 Apr 20.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s): Original Publication: London : Nature Publishing Group, copyright 2011-
Ιατρικοί όροι (MeSH): Attention*/physiology , Sleepiness*/physiology , Automobile Driving* , Deep Learning* , Detection Algorithms*, Humans ; Safety
Περίληψη: This research presents a robust real-time driver drowsiness detection system employing deep learning, attention mechanisms, and explainable AI (XAI) techniques to address this critical safety concern. The system integrates a fine-tuned InceptionV3 baseline with dual attention mechanisms, i.e., spatial and channel attention mechanisms, alongside a Low-Light Fine-Tuned LLFormer, to enhance detection performance in complex scenarios such as low-light conditions and occluded facial features. Additionally, the ResNet-50 model is utilized for feature extraction, while XAI techniques like Grad-CAM, LRP, etc., are incorporated to provide interpretability and transparency to model predictions. Multiple drowsiness indicators, including head tilting, blinking, and yawning, are analyzed using temporal factors, supported by facial landmark key point detection and a multi-browser distraction detection module for comprehensive monitoring. Experimental results reveal significant improvements, achieving up to 98.4% accuracy even under challenging conditions such as drivers wearing glasses, low light, and varied levels of facial occlusion. The model is optimized for real-time deployment on mobile and embedded platforms with minimal computational overhead. By incorporating these innovations, the proposed solution demonstrates the potential to significantly reduce drowsy driving-related risks, providing a practical, scalable, and interpretable tool for advanced driver assistance systems aimed at enhancing road safety.
(© 2026. The Author(s).)
Competing Interests: Declarations. Competing interests: The authors declare no conflict of interest. We, the authors, declare that the present paper has no known competing financial interests/assistance or personal relationships etc. which could appear to influence the work reported in this paper. Ethical approval: This study primarily utilizes publicly available benchmark datasets that were originally collected and released with informed consent from participants by their respective dataset providers. In addition, a small number of images used for qualitative illustration include facial images of two of the authors, for which written informed consent for participation in the study and for publication of identifiable images in this online open-access publication has been obtained. No participant or patient names appear in the manuscript, figures, tables, or captions. Consent for publication: All authors critically revised the manuscript and approved the final version for publication.
References: Korean J Anesthesiol. 2015 Dec;68(6):540-6. (PMID: 26634076)
Sensors (Basel). 2025 Jan 08;25(2):. (PMID: 39860697)
PLoS One. 2025 Feb 13;20(2):e0314541. (PMID: 39946342)
Neural Comput. 1997 Nov 15;9(8):1735-80. (PMID: 9377276)
Entry Date(s): Date Created: 20260420 Date Completed: 20260613 Latest Revision: 20260813
Update Code: 20260813
PubMed Central ID: PMC13260419
DOI: 10.1038/s41598-026-44442-3
PMID: 42010315
Βάση Δεδομένων: MEDLINE
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  – Url: https://dx.doi.org/doi:10.1038/s41598-026-44442-3
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  Data: Low-light driver drowsiness detection for real-time safety assistance using dual attention mechanisms in deep learning model.
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  Data: <searchLink fieldCode="AU" term="%22Saxena+S%22">Saxena S</searchLink>; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India.<br /><searchLink fieldCode="AU" term="%22Angel%22">Angel</searchLink>; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India.<br /><searchLink fieldCode="AU" term="%22Khurana+M%22">Khurana M</searchLink>; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India.<br /><searchLink fieldCode="AU" term="%22Tiwari+S%22">Tiwari S</searchLink>; Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India.<br /><searchLink fieldCode="AU" term="%22Shakya+HK%22">Shakya HK</searchLink>; Department of Artificial Intelligence and Machine Learning, School of Computer Science & Engineering, Manipal University Jaipur, Jaipur, India. harish.shakya@jaipur.manipal.edu.
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  Data: This research presents a robust real-time driver drowsiness detection system employing deep learning, attention mechanisms, and explainable AI (XAI) techniques to address this critical safety concern. The system integrates a fine-tuned InceptionV3 baseline with dual attention mechanisms, i.e., spatial and channel attention mechanisms, alongside a Low-Light Fine-Tuned LLFormer, to enhance detection performance in complex scenarios such as low-light conditions and occluded facial features. Additionally, the ResNet-50 model is utilized for feature extraction, while XAI techniques like Grad-CAM, LRP, etc., are incorporated to provide interpretability and transparency to model predictions. Multiple drowsiness indicators, including head tilting, blinking, and yawning, are analyzed using temporal factors, supported by facial landmark key point detection and a multi-browser distraction detection module for comprehensive monitoring. Experimental results reveal significant improvements, achieving up to 98.4% accuracy even under challenging conditions such as drivers wearing glasses, low light, and varied levels of facial occlusion. The model is optimized for real-time deployment on mobile and embedded platforms with minimal computational overhead. By incorporating these innovations, the proposed solution demonstrates the potential to significantly reduce drowsy driving-related risks, providing a practical, scalable, and interpretable tool for advanced driver assistance systems aimed at enhancing road safety.<br /> (© 2026. The Author(s).)
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  Data: Declarations. Competing interests: The authors declare no conflict of interest. We, the authors, declare that the present paper has no known competing financial interests/assistance or personal relationships etc. which could appear to influence the work reported in this paper. Ethical approval: This study primarily utilizes publicly available benchmark datasets that were originally collected and released with informed consent from participants by their respective dataset providers. In addition, a small number of images used for qualitative illustration include facial images of two of the authors, for which written informed consent for participation in the study and for publication of identifiable images in this online open-access publication has been obtained. No participant or patient names appear in the manuscript, figures, tables, or captions. Consent for publication: All authors critically revised the manuscript and approved the final version for publication.
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  Data: Korean J Anesthesiol. 2015 Dec;68(6):540-6. (PMID: <searchLink fieldCode="PM" term="%2226634076%22">26634076)</searchLink><br />Sensors (Basel). 2025 Jan 08;25(2):. (PMID: <searchLink fieldCode="PM" term="%2239860697%22">39860697)</searchLink><br />PLoS One. 2025 Feb 13;20(2):e0314541. (PMID: <searchLink fieldCode="PM" term="%2239946342%22">39946342)</searchLink><br />Neural Comput. 1997 Nov 15;9(8):1735-80. (PMID: <searchLink fieldCode="PM" term="%229377276%22">9377276)</searchLink>
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