Time Distributed MobileNetV2 with Auto-CLAHE for Eye Region Drowsiness Detection in Low Light Conditions

Driver drowsiness is a critical factor in road safety, contributing significantly to traffic accidents. This study proposes an innovative approach integrating Auto-CLAHE with Time Distributed MobileNetV2 to enhance drowsiness detection accuracy. This study leveraged the ULg Multimodality Drowsiness Database (DROZY) for facial expression analysis, focusing on the eye region. This study methodology involved segmenting videos into 10-second intervals, extracting 20 images per segment, and applying the Haar Cascade method for eye region detection. The Auto-CLAHE technique was developed to dynamically adjust contrast enhancement parameters based on image characteristics. The analysis yielded promising results. Integrating Auto-CLAHE with Time Distributed MobileNetV2 achieved a classification accuracy of 93.62%, outperforming traditional methods including Greyscale (92.55%), AHE (92.91%), and CLAHE (91.13%). Notably, a precision of 93.71% in detecting drowsiness, with a recall of 93.62% and an F1 score of 93.59% were obtained. Statistical analysis using ANOVA and Tukey HSD tests confirmed the significance of present study results. The key innovation of this study is the implementation of Auto-CLAHE, which significantly improves image contrast adaptation. This approach surpasses AHE and basic CLAHE in drowsiness detection performance, demonstrating remarkable robustness across diverse lighting conditions and facial expressions.

@article{Alzami2024,
  title     = {Time Distributed MobileNetV2 with Auto-CLAHE for Eye Region Drowsiness Detection in Low Light Conditions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Farrikh Alzami and Muhammad Naufal and Harun Al Azies and Sri Winarno and Moch Arief Soeleman},
  doi       = {10.14569/IJACSA.2024.0151146},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151146}
}