<![CDATA[Purpose – This study proposes a robust edge-computed Driver Monitoring System (DMS) using the YOLOv11 architecture to detect driver fatigue across daytime RGB and nighttime near-infrared (NIR) environments.Methods – A lightweight YOLOv11 model was trained on an augmented multi-spectral dataset of 2,289 images containing two critical fatigue markers: drowsy_eye and open_mouth. For real-time deployment on a resource-constrained Raspberry Pi 4B, the model was compiled into an optimized ONNX format with a 240 × 320 pixel input matrix. A Temporal State Machine using strict logical conjunction (AND logic) was integrated to process sequential frame updates and reduce false-positive alerts caused by micro-blinking.Findings – Under live multi-spectral stationary cabin hardware evaluation, the integrated prototype achieved real-time inference of 22.9–72.4 FPS in daytime RGB conditions and 20.7–28.7 FPS in nighttime NIR conditions. In total darkness, NIR feature extraction remained stable, with empirical confidence ranges of 0.70–0.82 for drowsy_eye and 0.93–0.94 for open_mouth. The state machine successfully confirmed microsleep events lasting more than two seconds and triggered synchronized voice alerts with a randomized LED array as a chaotic counter-fatigue sensory stimulus.Research implications – The system demonstrates the feasibility of deploying advanced AI-based DMS models on low-power, standalone, cloudless edge hardware for automotive safety applications.Originality – This study presents a multi-illumination RGB–NIR comparative evaluation of an ONNX-optimized YOLOv11 model integrated with an active randomized LED counter-fatigue intervention loop.]]>
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