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https://github.com/atharva309/drowsiness_detection_driving

Detect drowsiness in drivers through eyes and yawns
https://github.com/atharva309/drowsiness_detection_driving

image-classification image-detection yolov5

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Detect drowsiness in drivers through eyes and yawns

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# Drowsiness Detection for Driving



This project is aimed at detecting driver drowsiness by analyzing eye states (open/closed) and yawns using deep learning techniques. The model is based on YOLOv5 and performs both image classification and object detection to recognize drowsiness signs in real time.



## Project Overview



Drowsy driving is a significant factor in road accidents worldwide. Early detection of drowsiness can save lives by alerting the driver and taking appropriate preventive measures. This system identifies drowsiness by monitoring:



1. **Eye State**: Whether the driver’s eyes are open or closed.

2. **Yawns**: Detecting when the driver is yawning, which is a strong indicator of tiredness.



## Dataset



This project utilizes two key datasets:



- **[Eye Detection Dataset](https://universe.roboflow.com/ai-project-t1xm8/eye-detector-01g2k)**: Used for detecting eyes in different states (open or closed) with YOLOv5.

- **[Eye and Yawn Classification Dataset](https://www.kaggle.com/datasets/dheerajperumandla/drowsiness-dataset)**: Used for classifying eye states and detecting yawns for drowsiness analysis.



## How the Project Works



1. **Eye State Classification**:

   - The model classifies images as either "eyes open" or "eyes closed" to determine drowsiness.

   - This classification is crucial as prolonged eye closure is a common sign of drowsiness.

   - Two approaches were used for detecting eyes:

     - **Eye Detection Dataset**: A bounding box is drawn around the eyes using a specialized eye detection dataset.

     - **Face Detection Dataset**: A bounding box is drawn around the entire face, and the eyes are detected within that region.

2. **Yawn Detection**:

   - The YOLOv5 model is used to detect yawns, which often occur when a person is drowsy.

   - The model continuously monitors the driver's face for signs of yawning.

3. **Drowsiness Decision Logic**:

   - The system integrates the results from both eye state classification and yawn detection to make a final drowsiness prediction.

   - If the eyes are closed for a specified period or if yawns are detected frequently, the system triggers an alert.



## YOLOv5 for Drowsiness Detection



YOLOv5 (You Only Look Once) is a real-time object detection system. In this project, YOLOv5 is used for:

- **Eye Detection**: Identifying the position and state of the eyes (open or closed).

- **Yawn Detection**: Detecting when the driver is yawning using bounding boxes around the mouth region.

The model is trained to be lightweight and efficient, making it suitable for real-time deployment in vehicle systems.



## Example Outputs



Here are some sample outputs showing the detection of drowsiness indicators:



### using Object detection for eyes and Classification for yawns














### using image classification with face detection bounding box








## using image classification with eye detection bounding box








## Conclusion



This project highlights the application of deep learning for enhancing road safety. By integrating eye state and yawn detection, the system provides a robust method for detecting driver drowsiness and issuing timely alerts. Future improvements could involve adding more features like head pose estimation and facial landmark detection for even greater accuracy.



## References



- YOLOv5: [Ultralytics YOLOv5](https://github.com/ultralytics/yolov5)

- Dataset 1: [Eye Detection](https://universe.roboflow.com/ai-project-t1xm8/eye-detector-01g2k)

- Dataset 2: [Eye and Yawn Classification](https://www.kaggle.com/datasets/dheerajperumandla/drowsiness-dataset)