Project Overview
Wake Wave is a machine learning-based system designed to enhance road safety by detecting drowsiness in drivers. This project was built entirely from scratch, incorporating cutting-edge machine learning techniques to provide real-time detection and alerts, thereby preventing potential accidents caused by driver fatigue.
Our solution leverages facial landmark analysis, using real-time image processing to monitor driver behavior. By analyzing indicators such as eye closure. Wake Wave effectively determines drowsiness and sends timely alerts to ensure the driver's safety.
We have also built a website showcasing our project, giving users an overview of the system, its implementation, and its real-world application. Additionally, the website features a GPS system to track the driver’s location, and we plan to integrate a speed limit feature in the near future.
- Real-time Monitoring: Continuously monitors the driver's facial features using a camera and performs real-time analysis.
- Drowsiness Detection: Uses facial landmarks to track eye closure and calculates the Eye Aspect Ratio (EAR) to detect when the driver's eyes remain closed for a prolonged period.
- Alerts: Immediately notifies the driver through an alert system if drowsiness is detected, preventing potential accidents.
- GPS Integration: The website includes a GPS system that allows for real-time tracking of the driver's location, adding an extra layer of safety.
- Future Expansion: We plan to introduce a speed limit feature to monitor the driver's speed and ensure they are adhering to safe driving practices.
- Machine Learning from Scratch: We utilized Python, OpenCV, and Keras to build the entire model. The project includes face detection, eye classification, and real-time alerting.
- Website Integration: A comprehensive website showcasing the project and its capabilities.
- Image Capture: The system captures images from a webcam using OpenCV.
- Face Detection: The face is detected, and a Region of Interest (ROI) is created for focused analysis.
- Eye Detection: Eyes are detected within the ROI and analyzed to determine their state (open/closed).
- Classification: A Convolutional Neural Network (CNN) model classifies the eyes’ status based on the Eye Aspect Ratio (EAR).
- Alert System: If the eyes remain closed for a certain duration, the system triggers an alert, signaling drowsiness.
- GPS Tracking: The integrated GPS system provides real-time tracking of the driver’s location, enhancing safety by monitoring driving patterns.
- Future Features: A speed limit feature will be implemented soon to ensure the driver maintains a safe speed.
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Frameworks Used:
- Python for scripting
- OpenCV for image processing
- Keras with Convolutional Neural Networks (CNN) for classification
- Dlib's pre-trained shape predictor for facial landmark detection
- Haar cascade classifier for face and eye detection
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Model Architecture:
Our model is built using a CNN with multiple layers for extracting complex features from the input images. The final output layer uses the Softmax function to classify whether the driver is drowsy or alert.
The system effectively detects driver drowsiness by monitoring facial cues, with a special focus on eye behavior. It provides timely alerts in real-time scenarios, significantly contributing to road safety.
Our website showcases the Wake Wave system and its application in preventing accidents caused by drowsiness. Along with an overview of how the detection system works, we have integrated a GPS system that allows for real-time location tracking.
In the future, we plan to introduce a speed limit monitoring feature, which will ensure drivers are maintaining safe driving speeds.
Wake Wave aims to make roads safer by preventing accidents caused by drowsy driving. By utilizing machine learning techniques, facial landmark analysis, GPS tracking, and real-time alerts, Wake Wave offers a robust solution to a widespread issue. We look forward to further enhancing the system and integrating additional features such as speed limit monitoring for even greater driver safety.