Driver-Drowsiness-Detection-using-MQ6-gas-sensor-and-vision-sensor

Introduction: As per the National Highway Safety Administration Every year, more than a million car accidents occur as a result of driver inattention, resulting in more than 15,000 deaths and 50,000 critically injured people. According to the most recent data, street mishaps result in over 1.35 million passings and 20 to 50 million non-lethal wounds every year. As a consequence of the significant decrease in understanding of drivers of risk and acknowledgment of peril, laziness has become a central point in a large number of mishaps. Physical just as physiological changes lead to driver sleepiness. Actual changes include anxiety, while physiological changes incorporate the speed at which the driver moves and responds. When a driver is too exhausted to care about being cautious, this is known as driver exhaustion. Therefore, the driver may have a sluggish response time, diminished readiness, and weakened reasoning. In the most dire outcome imaginable, the driver would nod off steering the ship. To this end, examination into the acknowledgment of driver vernacular is basic for improving road traffic well being and is a wide and dynamic exploration field.. It is crucial to incorporate an effective, robust, and reliable drowsiness warning system that warns drivers before an accident occurs to avoid these accidents and deaths. In this project I have proposed an exclusive approach which can produce more accurate robust and reliable result as compare to others because in this approach I have measured drowsiness of driver with respect to different attributes like eye closure, yawn, gaze and the most important is the harmful gas leakage detection status where the sensor can sense seven different harmful gas inside the car and calculate the accordingly. For the front end part of this project I have used Flask micro web framework. Flask help me to deploy the model into the server and implement an advance front end design of the software for the user to get the alert in a more user friendly way using Flask & HTML CSS and JavaScript.