Authors - S. M. Mizanoor Rahman Abstract - Driver fatigue is a major cause of accidents on the road that generates major safety issues for drivers as well as passengers. Real-time detection of driver fatigue can help avert accidents by warning the driver about impending lapses in his attention. This paper proposes a real-time automated system for the detection of driver fatigue through observation of eye blink and yawn, which are major notifications for fatigue. The system uses a combination of deep learning models that give high accuracy levels in detecting a drowsy driver. Eye blink is detected by using a state-of-the-art object detection model that is trained to locate the open and closed states of the eyes accurately using correct coordinate mapping methods, giving an accuracy level of 96 percent. Yawning is detected using a combination of CNN and LSTM models that allow it to analyze spatial information as well as temporal information obtained through videos, giving an accuracy level of 98 percent. Both of these modules work on real-time camera inputs, which makes it possible for a constant monitoring of the alertness of the driver. Whenever the driver is found dozing off due to either excessive blinking or yawning, the system releases a real time auditory warning alert to caution the driver. The result of the experiments has justified that the capability of the combined system works well while operating reliability with low-latency responses in real time. This study has shown that the hybrid detection strategy with spatial and temporal analysis is quite effective in detecting a dozy driver on the road and developing such a system that can be helpful in increasing the safety of the road.
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