The Driver's Drowsiness Android app utilizing the YOLOv5s model of computer vision is a groundbreaking solution designed to enhance road safety by detecting and alerting drivers about potential instances of drowsiness. Leveraging the cutting-edge YOLOv5s architecture, the app employs state-of-the-art object detection techniques to accurately identify Eyes Close and Open and Yawn (these are sign of Drowsiness) in real-time.
Through advanced deep learning algorithms, the app continuously monitors the driver's facial features, including eye movements, eyelid closures, and head positions, to assess their level of alertness behind the wheel. With YOLOv5s' robust performance and efficiency, the app ensures rapid and precise detection of drowsiness-related cues, enabling timely intervention to prevent accidents caused by driver fatigue.
The app's intuitive interface provides users with seamless access to vital information regarding their alertness status, with clear visual indicators and customizable alert thresholds. In the event of detected drowsiness, the app delivers immediate alerts through audible alarms, vibration notifications, and visual prompts, effectively notifying the driver to take necessary corrective actions or pull over for rest.
Moreover, the app incorporates sophisticated machine learning capabilities to adapt and refine its drowsiness detection algorithms over time, enhancing accuracy and reliability across diverse driving conditions and individual characteristics. By continuously learning from user interactions and feedback, the app ensures optimal performance and responsiveness, thereby maximizing its effectiveness in preventing accidents and promoting safer driving habits. <br?
The integration of the YOLOv5s model into the Driver's Drowsiness Android app represents a significant advancement in the field of computer vision-driven safety solutions, offering unparalleled accuracy, speed, and versatility in detecting and mitigating the risks associated with driver fatigue. With its innovative features and robust architecture, the app stands as a testament to the potential of artificial intelligence in safeguarding lives on the road and fostering a culture of responsible driving.
- Android Device with minimum sdk 21
- Processing Power and Memory (RAM) at least 2GB of RAM is recommended, although 4GB or more would be ideal for better performance
- Camera Quality with a resolution of 720p or higher would be suitable for optimal performance.
- Andriod Studio
- Pycharm or Visual Studio code
- Roboflow
- Opencv Library
- library
- Android Studio
demo_video.mp4
- https://www.tensorflow.org/lite/android/tutorials/object_detection
- https://en.wikipedia.org/wiki/Driver_drowsiness_detection
This project is licensed under the MIT License. See the LICENSE file for details.
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