ckcl/driver-drowsiness-detector

Driver Drowsiness Detection Model

This model is designed to detect driver drowsiness from facial images using a CNN architecture.

Model Details

• Architecture: CNN
• Input: Facial images (64x64x3)
• Output: Binary classification (drowsy/not drowsy)

Usage

import tensorflow as tf
import cv2
import numpy as np

# Load model
model = tf.keras.models.load_model('drowsiness_model.h5')

# Preprocess image
img = cv2.imread('face.jpg')
img = cv2.resize(img, (64, 64))
img = img / 255.0
img = np.expand_dims(img, axis=0)

# Make prediction
prediction = model.predict(img)
is_drowsy = prediction[0][0] > 0.5

Training Details

• Dataset: Custom driver drowsiness dataset
• Training method: Binary cross-entropy loss with Adam optimizer
• Validation split: 20%
• Early stopping with patience=3

Model Architecture

• Input Layer: 64x64x3 images
• Convolutional Layers:
  • Conv2D(32, 3x3) + BatchNorm + ReLU
  • MaxPooling2D(2x2)
  • Conv2D(64, 3x3) + BatchNorm + ReLU
  • MaxPooling2D(2x2)
  • Conv2D(128, 3x3) + BatchNorm + ReLU
  • MaxPooling2D(2x2)
• Dense Layers:
  • Dense(128) + BatchNorm + ReLU
  • Dropout(0.5)
  • Dense(1) + Sigmoid

Performance

• Binary classification for drowsiness detection
• Optimized for real-time inference
• Suitable for embedded systems and edge devices

License

This model is released under the MIT License.