Driver Drowsiness Detection System Using Deep Learning

Overview

Driver drowsiness is one of the leading causes of road accidents. This project is an AI-powered driver drowsiness detection system that uses Deep Learning and Computer Vision to identify signs of driver drowsiness in real time. The application captures live video through a webcam, processes the frames, and predicts whether the driver is drowsy or alert.

Problem Statement

Road accidents caused by driver fatigue are a major safety concern. The objective of this project is to develop an intelligent system that can monitor the driver's condition in real time and help improve road safety by detecting signs of drowsiness.

Features

Real-time webcam monitoring
AI-based drowsiness detection
CNN-based image classification
Real-time prediction display
User-friendly interface
Computer Vision application

Technology Stack

Programming Language

Python

Libraries & Frameworks

TensorFlow
OpenCV
NumPy
Matplotlib

Deep Learning Model

Convolutional Neural Network (CNN)

Dataset

The model was trained using a publicly available driver drowsiness dataset from Kaggle. The dataset was preprocessed before training by resizing images and normalizing pixel values to improve model performance.

Project Workflow

Collect the driver drowsiness dataset from Kaggle.
Preprocess the images.
Train the CNN model.
Save the trained model.
Capture live webcam frames using OpenCV.
Process each frame and make predictions.
Display the prediction in real time.

Challenges Faced

During development, I faced challenges with maintaining consistent predictions under different lighting conditions and camera positions. I improved the preprocessing pipeline and tested the model with different inputs to achieve more reliable results.

What I Learned

This project helped me gain practical experience in:

Deep Learning
Convolutional Neural Networks (CNN)
Computer Vision
TensorFlow
OpenCV
Image Preprocessing
Model Training and Evaluation
Real-time AI Applications

Future Improvements

Add an audio alert when drowsiness is detected.
Improve prediction accuracy using a larger and more diverse dataset.
Enhance real-time performance.
Deploy the application on the cloud.
Develop a mobile-friendly version.

Conclusion

This project demonstrates how Artificial Intelligence and Deep Learning can be used to develop an intelligent driver monitoring system. It strengthened my understanding of TensorFlow, OpenCV, image preprocessing, CNN model development, and real-time AI applications while working with a publicly available Kaggle dataset.