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Write a conclusion in detail of Drowsiness detection of driver using Deep Neural Networks.

Write a conclusion in detail of Drowsiness detection of driver using Deep Neural Networks.

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Answer #1

Drowsiness detection of driver using Deep Neural Networks.

CONCLUSION:

*The main reasons for motor vehicular accidents is related to driver's inattention or drowsiness.

* A drowsiness detector on a car can reduce numerous accidents.

*It is specifically designed for embedded systems such as Android mobile. The role of the system is to detect facial landmark from images and deliver the obtained data to the trained model to identify the driver's state.

*The purpose of the method is to reduce the model's size considering that current applications cannot be used in embedded systems due to their
limited calculation and storage capacity.

* According to the experimental results, the size of the used model is small while having the accuracy rate of 81%.

* Hence, it can be integrated into advanced driver-assistance systems, the Driver drowsiness detection system, and mobile applications.

* However, there is still space for the performance
improvement. The further work will focus on detecting the distraction and yawning of the driver.

I hope the answer is clear.Thank you

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