Avoidance of Accidents through Detection of Drowsiness during Driving using Convolution Neural Network
DR.GLADRENE SHEENA BASIL, J.Sahaya Jeniba, A. Ajay Krishnan, M.Lokeshwaran, R Kapin, N Hariprasath
Convolution Neural Network, Prevention, Accident, Drowsiness, Driving
One of the most frequent causes of road accidents is due to the driver’s drowsiness. Statistics show that drowsiness expose driver to higher crash risks, severe physical injuries, or even death. Tiredness may cause drowsiness which is a state of decreased mental alertness where the driver is no longer safe. Fatigue not only puts the driver himself at risk, but also puts the other participants such as pedestrians and other travelers on road at jeopardy. Due to high variability of surrounding parameters, current techniques developed for this area have several limitations. Poor lighting might affect the ability of the camera to measure the face and the eye of the driver, accurately. This will affect the analysis due to late detection or no detection and thereby decrease the accuracy and efficiency of the technique. In this paper, an intelligent system is developed to detect the drowsiness of the driver and thus prevent accidents, save money and reduce losses and suffering It proposes a real-time system that utilizes computerized camera to automatically track and process driver's eye using Python, and Convolution Neural Network (CNN).
Article Details
Unique Paper ID: 160928

Publication Volume & Issue: Volume 10, Issue 2

Page(s): 98 - 103
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