An Approach for Detecting Drowsy Drivers in Vehicle using CNN Techniques
Ch. Prathyusha1, Mrutyunjaya S Yalawar2

1Ch Prathyusha, Department of Computer Science and Engineering, CMR Engineering, (CMREC) College, Hyderabad (Telangana), India.
2Mrutyunjaya S Yalawar, Department of Computer Science and Engineering, CMR Engineering (CMREC) College, Hyderabad (Telangana), India.

Manuscript received on 31 July 2022 | Revised Manuscript received on 04 August 2022 | Manuscript Accepted on 15 September 2022 | Manuscript published on 30 September 2022. | PP: 41-44 | Volume-11 Issue-3, September 2022. | Retrieval Number: 100.1/ijrte.C72460911322 | DOI: 10.35940/ijrte.C7246.0911322
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Abstract: On average, 1200 accidents happen on Indian roadways every day, resulting in 400 fatalities and numerous injuries. Accidents brought on by sleepiness can be the result of fatigue or intoxication. Long periods of driving or drinking can make drivers drowsy, which is their main source of distraction. can make drivers drowsy, which is their main source of distraction. There’s a chance that this diversion will lead to. The driver, additional passengers, and pedestrians were also murdered in addition to those in the other vehicles. along a highway A driver’s negligence on the road could lead to their own demise, the deaths of others, and a challenging scenario for those people’s families. To avoid such accidents, I proposed a system that alerts the driver if she or he begins to feel sleepy. We put the approach into practice by employing a machine learning model based on computer vision. The driver’s face is fed into a classification algorithm that has been trained on images of non-drowsy and drowsy faces. This algorithm uses landmark detection to determine whether the face is sleepy or awake. The system generates an alarm if the driver’s face is sleepy. The alarm can alert the driver that he or she is drowsy and allow the driver to take the necessary actions. So, in order to avoid these accidents, we will create a system using Python, OpenCV, and Keras that will alert the driver if he feels unsafe. sleepy. Drowsiness detection is a safety technology that can help prevent accidents caused by drivers who nodded off while driving. 
Keywords: Convolutional Neural Networks, Drowsy driver, Drowsiness detection, Machine learning.
Scope of the Article: Machine learning