# Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny
## 1 Introduction  
Research conducted by the AAA Foundation for Traffic Safety reveals that between 6% and 11% of all motor-vehicle accidents reported to police, and from 16% to 21% of fatal accidents, may involve drowsy driving [1]. This statistic underscores the urgent need for the implementation of appropriate systems designed to detect driver fatigue. Such systems, by monitoring fatigue indicators and issuing alerts in real time, play an essential role in reducing the risk of drowsy driving, with the potential to significantly lower the incidence of traffic accidents and related fatalities.  
In the domain of vehicular safety, the current research is focused on reducing accidents caused by driver somnocence through three primary detection methodologies: physiological metrics (including electroencephalography (EEG) [2, 3], electrovoculography [4], and multi-modal algorithms [5]); vision-based methods (tracking eyelid movement, yawning, and head orientation) [6, 7, 8, 9, 10]; and vehicular dynamics analysis (such as lateral position and speed variation) [11, 12]. Although vehicular dynamics offer an indirect measure of alertness, physiological and optical methods provide a more direct assessment. Physiological markers, especially EEG patterns indicating alpha wave dominance, are sensitive but may be considered invasive. In contrast, image analysis of eye, mouth, and head movements is non-invasive and increasingly favored for its practicality in real-life applications, striking a balance between effectiveness and user comfort.  
In the specialized area of driver drowsiness detection through image analysis, research has concentrated on eye blink detection, facial landmark analysis, and facial expressionanalysis using classifier models. Various deep learning models have been successfully employed in eye blink detection [13; 14]. Facial landmark analysis has also received attention, with technologies like MTCNN (Multi-task Cascaded Convolutional Neural Network) for face detection and classification systems achieving high accuracies in identifying drowsiness [15; 16]. The exploration of 3D-CNN (Three-dimensional Convolutional Neural Network) integrated with LSTM (Long Short-term Memory) and other CNN architectures for facial expression analysis has yielded high accuracy rates on various datasets [17; 18; 19; 20].  
However, image analysis-based methods for drowsiness detection face challenges, including variability in ocular surface texture across different ethnicities, influenced by anatomical, dermal, and external factors like lighting conditions and eyewear. Additionally, the need for real-time processing of complex visual data demands substantial computational resources, posing an obstacle to wider adoption.  
This study introduces a real-time driver fatigue detection system utilizing an optimized You Only Look Once version 7-tiny (YOLOv7-tiny) model. YOLOv7-tiny, chosen for its real-time processing capabilities and efficiency, is ideal for in-vehicle systems without high computational demands [21; 22; 23; 24]. This model was selected based on its superior performance in diverse and challenging visual environments, demonstrating robust adaptability to various lighting conditions and head poses. These attributes are critical in dynamic driving contexts where changes in environmental light and driver movement are frequent. Furthermore, the implementation of structured pruning and parameter adjustments within the YOLOv7-tiny framework has been strategically employed to refine the model's operational efficiency, thus ensuring that performance optimization does not come at the expense of detection accuracy. Performance metrics such as precision, recall, and mean average precision (mAP) are used to evaluate the model's effectiveness post-pruning, ensuring the balance between accuracy and computational efficiency for deployment in resource-constrained settings.