Adverse Environment - Driving Safety with Deep Learning
Pruthviraj Sanjay Pawar, Sudarshan Abasaheb Haral, Kartik Chabaji Jadhav
Machine Learning, Detection, Driving Safety, Neural Network
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
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews