The YOLO(YOU ONLY LOOK ONCE) model leverages deep CNNs for real-time driver fatigue detection. By analyzing in-car camera footage, it provides instant alerts to the driver when signs of drowsiness are detected, thereby enhancing road safety significantly.
My project is focused on enhancing road safety by detecting driver drowsiness / sleepiness in real-time using the YOLO (YOU ONLY LOOK ONCE) model. We leverage the power of deep Convolutional Neural Networks (CNNs) to analyze in-car camera footage continuously. This advanced technology allows us to provide instant alerts to the driver whenever signs of drowsiness or sleepiness are detected.
To achieve this, we have employed a Jupyter Notebook environment, where we import various libraries and tools, including OpenCV2 and others, to facilitate the implementation of our driver fatigue detection system. OpenCV2 helps us capture and process the live video feed from the in-car camera, allowing us to feed it into the YOLO model for real-time analysis.
Our project combines cutting-edge machine learning techniques with practical applications, making it an invaluable tool for improving road safety. By alerting drivers promptly to signs of drowsiness, we aim to significantly reduce the risks associated with fatigue-related accidents and contribute to safer roads for everyone.
