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ENHANCED ROAD SAFETY
Client’s Need
Improved road safety by monitoring drowsiness while driving in public transport.
Accurate detection of drowsiness indicators, including facial expressions, eye movements, and face orientation, in real-time. Integration with Amazon ALEXA.
Goals of the project
Develop a deep learning-based system for accurate drowsiness detection in drivers.
Implement real-time monitoring and alerting on the Android platform.
Integrate voice-enabled features using AWS Alexa for convenient driver interaction and communication.
Voice enabled driver interaction for alerts, breaks and safety advice.
Our Approach
Utilize EfficientNet deep learning model for drowsiness indicators such as facial expressions, eye movements, and face orientation.
Implement Andriod library ”ML Kit” for face orientation and expression detection to enhance accuracy and efficiency.
Integrate the solution on the Android platform for real-time monitoring and alerting.
Integrate AWS Alexa for voice-enabled interaction, enhancing user convenience and system effectiveness.
Outcome
97% accuracy in drowsiness detection.
Enhanced driver safety, reduced accident risks.
Easy scalability and deployment as its based on Andriod.
Implemented a highly convenient voice-enabled user interface.
Ensured minimal driver distraction while interacting with the system.
Published:
September 28, 2024
Category:
Edge Computing
Client:
Oceanthemes
