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Add YOLOv5-Based Drowsiness Detection Model Using OpenCV
Purpose
We propose adding a new Computer Vision model for drowsiness detection using YOLOv5 and OpenCV to the DL-Simplified repository. This enhancement aims to provide a practical application for detecting drowsiness in various scenarios, such as monitoring driver alertness.
Benefits and Impact
Enhanced Functionality: This model will extend the repository's capabilities by introducing a state-of-the-art drowsiness detection system.
Practical Applications: The model can be used in real-time applications to ensure safety by monitoring and alerting drowsiness, particularly useful in automotive safety systems.
Technical Requirements
YOLOv5: Utilized for object detection and classification.
OpenCV: Used for real-time video processing and capturing data.
Integration Steps
Model Development: Implement the YOLOv5 model for drowsiness detection.
Data Collection: Use OpenCV to capture and process video data for training and detection.
Model Training: Train the model with relevant datasets to detect signs of drowsiness.
Evaluation: Test the model's accuracy and performance in real-time scenarios.
This addition will significantly enhance the repository's offerings and provide a valuable resource for contributors interested in practical deep learning applications.
The text was updated successfully, but these errors were encountered:
Add YOLOv5-Based Drowsiness Detection Model Using OpenCV
Purpose
We propose adding a new Computer Vision model for drowsiness detection using YOLOv5 and OpenCV to the DL-Simplified repository. This enhancement aims to provide a practical application for detecting drowsiness in various scenarios, such as monitoring driver alertness.
Benefits and Impact
Technical Requirements
Integration Steps
This addition will significantly enhance the repository's offerings and provide a valuable resource for contributors interested in practical deep learning applications.
The text was updated successfully, but these errors were encountered: