Driver drowsiness detection and traffic speed sign recognition for accident prevention using machine learning

The main aim of the project is to detect the driver drowsiness level, Traffic sign detection, Alcohol consumption detection, Accident occurred information detection and to control the vehicle using the above parameters as input with help of yolo machine learning algorithm. Opencv, Keras, Tensor flow...

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Bibliographic Details
Main Authors: Yuvaraj, M., Bharath, G., Maheshwaran, S., Saravanan, M.
Format: Conference Proceeding
Language:English
Subjects:
Accident prevention
Algorithms
Driver fatigue
Machine learning
Sleepiness
Tensors
Traffic signs
Traffic speed
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Description
Summary:The main aim of the project is to detect the driver drowsiness level, Traffic sign detection, Alcohol consumption detection, Accident occurred information detection and to control the vehicle using the above parameters as input with help of yolo machine learning algorithm. Opencv, Keras, Tensor flow libraries and CNN model are the additional support technique used to detect and control the vehicle. According to the government rule the vehicle should follow the traffic sign boards but many of us are not considering it and drunk and drive is also a major issue. So by making it automatic detection system the accidents can be reduced.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0179869