Drowsiness is a major cause of vehicular and in
dustrial accidents, motivating the need for real-time monitoring
systems that are accurate, non-intrusive, and deployable on low
power platforms. While physiological signal–based approaches
(e.g., EEG/EOG) provide high reliability, their dependence on
sensor attachment makes them intrusive for everyday use.
Camera-based solutions are more practical but face two key
challenges.
First, state-of-the-art deep learning–based face recognition and
drowsiness detection models require substantial computational
and memory resources, making them unsuitable for embedded
deployment. Popular architectures such as DeepFace, Keras-Face,
and VGG-Face are extremely accurate but Cloud-scale. Existing
approaches for camera based drowsiness detection often rely
on heavy convolutional or attention-based backbones, resulting
in large parameter footprints and high operational complexity.
Deploying these models on embedded systems typically leads to
significant accuracy degradation. Second, traditional handcrafted
pipelines, although lightweight, depend heavily on face detection
and landmark extraction, making them fragile under illumination
changes, occlusions, and multi-view variations. Our experiments
show that even extended handcrafted feature pipelines struggle
to exceed 75–82% accuracy under realistic conditions.
To address these limitations, we propose a compact end
to-end CNN architecture enhanced with Convolutional Block
Attention Module (CBAM). The proposed model achieves 99.05%
accuracy on YAWDD and94.65%onNTHU-DDD,outperforming
transfer-learning backbones such as MobileNetV2, ResNet-50,
and EfficientNet-B0. Furthermore, component wise performance
analysis confirms the effectiveness of CBAM-based attention
refinement, while computational analysis shows that the model
achieves superior predictive performance with only 0.539M
parameters, enabling an effective balance between accuracy,
compactness, and execution efficiency for resource-constrained
drowsiness monitoring systems.