Fatigue driving detection method based on deep learning

Peilong Lu; Run Xue; Dingkai Li; Zhengyi Ma

doi:10.1117/12.2678808

14 April 2023 Fatigue driving detection method based on deep learning

Peilong Lu, Run Xue, Dingkai Li, Zhengyi Ma

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340E (2023) https://doi.org/10.1117/12.2678808
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

In order to avoid fatigue driving, the driver fatigue detection technology is studied by extracting facial fatigue feature parameters. Use the optimized SSD to extract facial features, use PFLD to detect key points of the face, and detect the key points and spatial attitude angles of the eyes, mouth, and head of the face; calculate the face fatigue feature parameters based on time series The matrix is input to GRU for fatigue driving detection. Compared with other eight methods in the case of low computing power, it has a high accuracy rate and detection speed, which meets the needs of the fatigue driving detection system.

Citation Download Citation

Peilong Lu, Run Xue, Dingkai Li, and Zhengyi Ma "Fatigue driving detection method based on deep learning", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340E (14 April 2023); https://doi.org/10.1117/12.2678808

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