Multi-Feature fatigue detection method for bus drivers based on YOLOv8n-SimAM with integrated temporal information

Fatigue driving represents a major cause of road traffic accidents, with particularly severe consequences in passenger transport. Developing accurate fatigue detection methods resilient to in-vehicle environmental interference is crucial for enhancing road safety. This study proposes a multi-feature fatigue detection framework integrating temporal information based on YOLOv8n-SimAM for bus drivers. The SimAM attention mechanism enhances feature extraction from ocular and oral regions while suppressing cabin background interference. By incorporating the driver’s eye and mouth states with temporal information, we established a multi-feature fatigue reasoning mechanism. Comparative experiments demonstrate that YOLOv8n-SimAM achieves superior performance in precision, recall, and P_mA50-95 (mean average precision across IoU thresholds 0.5–0.95) over alternative models. Our approach achieves enhancements of 2.8% in precision, 5.4% in recall, and 7.5% in F_1-score (harmonic mean of precision and recall) over the suboptimal benchmark, without a substantial increase in model parameters. Ablation studies confirm that integrating SimAM reduces false positive rate by 48%, while the temporal fatigue inference module decreases missed detection rate by 72%. The integrated model attains an F_1-score of 0.906, an accuracy of 0.9, with corresponding missed detection and false positive rates of 0.111 and 0.087, respectively. This methodology enables reliable facial feature capture and fatigue assessment in challenging in-cabin environments, providing an effective solution for off-site monitoring of bus driver fatigue.