Abstract
With the continuous development of social productivity, automobiles have gradually become an indispensable means of transportation for families, and at the same time, the traffic accident rate caused by fatigue driving is also rising. Based on this, this paper proposes a fatigue driving detection and early warning method based on the fusion of multi-source information such as eyes, mouth, and head. Locate the face position and fatigue judgment feature points, and use different judgment criteria for fatigue detection according to the detected driver's eyes, mouth, head posture and other information, and finally fuse the detection information to obtain judgment results and give early warnings. Compared with the traditional convolutional neural network, the task cascade convolutional neural network improves the detection accuracy by 20%.
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Acknowledgment
This work was supported by The Innovation and Entrepreneurship Fund for college students of Hubei University of Automotive Technology--Real time fatigue driving detection and early warning system based on multi-source information fusion (DC2021031) and Research on surface defect detection of auto parts based on deep learning (B2020080).
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Fan, Y., Peng, G., Che, K., Rao, H. (2023). Fatigue Driving Detection and Early Warning System Based on Multi-source Information Fusion. In: Wang, Y., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XII. IWAMA 2022. Lecture Notes in Electrical Engineering, vol 994. Springer, Singapore. https://doi.org/10.1007/978-981-19-9338-1_1
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DOI: https://doi.org/10.1007/978-981-19-9338-1_1
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Online ISBN: 978-981-19-9338-1
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