SMARTSIGHT: YOLO-ENABLED OBJECT DETECTION FOR ENHANCED VISUAL ASSISTANCE

Abstract

Visual impairment poses significant challenges to independent mobility, environmental awareness, and safety. With recent advances in computer vision and deep learning, intelligent assistive systems have emerged as promising solutions to enhance the quality of life for visually impaired individuals. This study introduces SmartSight, a real-time vision assistance framework leveraging the YOLO (You Only Look Once) object detection algorithm to identify and classify objects with high accuracy and speed. The proposed system integrates object recognition, distance estimation, and contextual alerts to provide users with situational awareness through audio feedback. Unlike conventional assistive tools, SmartSight combines lightweight computation with robust detection, making it suitable for portable devices and edge-based applications. Experimental results demonstrate the system’s ability to detect multiple objects simultaneously in dynamic environments, offering reliable performance in both indoor and outdoor scenarios. By enhancing accessibility, safety, and autonomy, SmartSight represents a significant step forward in applying deep learning technologies to create inclusive smart assistive solutions for the visually impaired