Adverse Environment - Driving Safety with deep learning

In this system, we proposed to reduce the number of accidents caused by driver fatigue and thus improve road safety.This system treats the automatic detection of driver drowsiness based on visual information and artificial intelligence.We locate, track and analyze both the driver face and eyes to measure PERCLOS (percentage of eye closure) with SoftMax for neural transfer function.it will be also using alcohol pulse detection to check out the person is normal or abnormal.Driver's fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in occupied This system aims to make the driving vehicle safer and protect from drowsiness, alcohol detection avoids accidents and collision between the vehicles while driving and minimizes the road accidents.This product will come to prepare a combination of face detection and face contours along with an additional feature of alcohol consumption of driver and as an accordance, the vehicle acceleration is kept.This product consists of deep learning algorithms.The face will detect using computer vision and forms contours around the face.The person is checked with drowsiness detection then alcohol detection through a set of the camera.The set of device checks for alcohol parameters taken by the person.The device used in this paper uses a display interface to show and notify alertness.It messages the concerned person to pick up the person who is being alcoholic.OpenCV library is being used to facilitate face drowsy detection.