Preventing Drowsy-Driving Accidents Using Convolutional Neural Networks - Full walkthrough with Keras, Face Recognition, OpenCV, & PIL.
Full walkthrough with Keras, Face Recognition, OpenCV, & PIL
The National Highway Traffic Safety Administration estimates that there are 91,000 crashes involving drowsy drivers a year that lead to an estimated 50,000 injuries and nearly 800 deaths. Additionally, 1 in 24 adult drivers report having fallen asleep at the wheel in the past 30 days. Research has even found that going more than 20 hours without sleep is the equivalent of having a blood-alcohol concentration of 0.08% — the U.S. legal limit.
Because of this serious problem, A group of other data scientists and I set out to develop a neural network that can detect if eyes are closed, and when applied in tandem with computer vision, to detect if a live human has had their eyes closed for more than a second. This sort of technology is useful for anybody interested in increased driving safety, including commercial & everyday drivers, car companies, and vehicle-insurance companies.
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