Autonomous Driver Distraction Identification using the YOLO Algorithm in Deep Learning

This research work proposes a novel approach to driver distraction detection using the You Only Look Once (YOLO) object detection algorithm combined with a convolutional neural network (CNN). The proposed model comprises two main stages: object detection employing the YOLO and categorization for identifying objects. This algorithm is utilized for detect and locate various objects in the driver's environment, including the driver's face, hands, and other potential distractions. Following this, a CNN classifies the detected objects to ascertain whether the driver is distracted. The proposed model is evaluated using a public dataset and demonstrates high accuracy in detecting driver distraction. This method has the potential for integration into advanced driver assistance systems, thereby improving road safety in real-time environments.