AI-Powered Road Safety: Detecting Driver Fatigue through Visual Cues

Driving when fatigued is among the main causes of road deaths. Consequently, one ongoing research area is how to recognize driver fatigue and how to determine whether it is present. A large percentage of conventional methods are either based on machines, the behavior of people, or physiological processes. Some solutions need expensive sensors and data processing, while others are infiltrating and uncomfortable to the driver. As a consequence, this study creates an accurate, real-time method for identifying driver fatigue. The footage is captured by a camera, and image processing techniques are employed to recognize the driver's face in each frame. When facial landmarks on the detected face are pointed, the eye aspect ratio and mouth opening ratio are computed based on their values, and drowsiness is recognized utilizing generated adaptive thresholding. The following stage involves determining whether or not a discovered item is a face using SVM. It also checks the driver's eye aspect ratio (EAR) and mouth opening ratio (MOR) up to a predetermined number of times to look for signs of sleepiness and yawning. If sleepiness is identified, a warning email is sent to the registered email address