Adverse Environment - Driving Safety with Deep Learning

Adverse Environment - Driving Safety with Deep Learning
Author(s):
Pruthviraj Sanjay Pawar, Sudarshan Abasaheb Haral, Kartik Chabaji Jadhav
Keywords:
Machine Learning, Detection, Driving Safety, Neural Network
Abstract
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 mouth closure) with neural network transfer. This program will come to prepare a combination of face detection and face contours 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 through a set of the camera. The program 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.
Article Details
Unique Paper ID: 165093 Publication Volume & Issue: Volume 11, Issue 1 Page(s): 29 - 34
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NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

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