A fatigue driving inspection system based on image recognition

Driver fatigue is a significant contributing factor to traffic accidents. Using image recognition technology to warn against fatigue driving can significantly reduce the incidence of accidents and ensure road traffic safety. This paper investigates a fatigue driving detection system based on image recognition technology, aiming to monitor the driver's fatigue status in real-time through computer vision and machine learning techniques. The study concludes that current research methods include installing a camera in front of the driver to capture facial images. Preprocessing steps such as grayscale conversion and Gaussian filtering enhance image analysis accuracy. Deep learning algorithms are used to detect facial landmarks and calculate the eye aspect ratio (EAR) and other fatigue parameters (e.g., PERCLOS) to determine the driver's fatigue state. The research results indicate that this system performs well in terms of detection accuracy, real-time capability, and adaptability, effectively identifying and warning against fatigue driving, thereby reducing the occurrence of traffic accidents.