Fatigue driving detection based on facial state recognition

Yue Wu; Jialing Liu

doi:10.1117/12.2681174

23 May 2023 Fatigue driving detection based on facial state recognition

Yue Wu, Jialing Liu

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452N (2023) https://doi.org/10.1117/12.2681174
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Fatigue driving detection is of great significance to prevent fatigue driving and reduce the occurrence of traffic accidents. The existing fatigue driving detection method based on driver facial information has low robustness, and its speed and accuracy can still be improved. A method of fatigue driving detection based on end-to-end facial state recognition model is proposed. Based on improved YOLOv7 network, the end-to-end facial state recognition model can detect the driver’s eyes and mouth and recognize the state of these two parts directly from driver’s image. Then, according to the state of eyes and mouth, the driver's fatigue state is judged based on two criterions: the PERCLOS and the duration of yawning. Experimental results show that the proposed method can guarantee high recognition rate in complex environment and has higher real-time performance.

Citation Download Citation

Yue Wu and Jialing Liu "Fatigue driving detection based on facial state recognition", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452N (23 May 2023); https://doi.org/10.1117/12.2681174

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