Focus Assist: Enhancing Road Navigation with Improved Awareness Using AI
Authors:-Dr.G.Sumathi, S.Harini, R. Sowmya, P.A.Srivarsha
Abstract- Distracted driving is a major global contributor to traffic accidents. Distracted drivers are three times more likely than non-distracted drivers to be involved in an accident. For this reason, enhancing road safety requires the ability to identify driver distraction. Numerous earlier research have put forth a variety of techniques for detecting driver distraction, including approaches based on images, sensors, and machine learning. However, the accuracy, complexity, and real-time performance of these approaches are limited. Using a convolutional neural network (CNN) and the You Only Look Once (YOLO) object detection algorithm, this work suggests a unique method for driver distraction detection. The two primary phases of the suggested model are the classification of the items that are discovered and object detection utilizing YOLO. The driver’s face, hands, and other items that could distract them are among the many elements in their environment that the YOLO algorithm is used to identify and find.Next, a CNN is used to categorize the things that have been spotted in order to ascertain whether or not the driver is distracted. The suggested model performs well in identifying driver distraction when tested on a public dataset. Utilizing the CNN algorithm, assess the driver’s level of sleepiness based on ocular attributes. The integration of the suggested technique into sophisticated driver assistance systems has promise for enhancing road safety in real-time conditions.
