A vision-based drowsiness detection system for railway operators using lightweight convolutional neural networks

This research addresses the challenge of monitoring railway driver drowsiness using a real-time, vision-based system powered by convolutional neural networks, specifically the YOLOv8 architecture including attention mechanisms. The core idea is to keep the eye on subtle facial features like eyelid closure durations as indicators of fatigue. The model is designed to be lightweight for fast processing, which is critical for real-time applications. To build the model, a custom dataset of 6,991 frames was compiled. It also boosted the dataset’s diversity using data augmentation, improving the model’s robustness against real-world variability. And it paid off: the system hit an overall accuracy of 96.8%, precision of 97.28%, and recall of 97.46%, which is impressive, especially under different lighting conditions. The system works best in low sunlight. When strong solar glare kicks in, detection dips, showcasing the impact environmental factors can have on vision-based systems. In short, this study highlights how deep learning can realistically enhance railway safety by alerting operators before drowsiness leads to incidents. For future work, the plan was to toughen up the system to handle tough lighting better and explore combining vision with other sensor types (e.g., electroencephalography) for a fuller fatigue picture. Discussion about particular cognitive brain computer interface and health issues as anemia for further studies.