YOLO SIGHT: A Real-Time Object Detection and Distance Estimation System

Abstract

This paper presents YOLO Sight, an advanced real-time computer-vision framework that combines object detection, distance estimation, and auditory feedback to enhance situational awareness. The system integrates the YOLOv8 deep-learning model with OpenCV and a Text-to-Speech (TTS) engine to provide both visual and verbal responses for detected objects. Designed for assistive, surveillance, and robotic applications, YOLO Sight achieves 92 % detection accuracy with 83 % F1-score and operates at up to 20 FPS on mid-range hardware, ensuring affordability, accessibility, and scalability for practical deployment.
Keywords : YOLOv8, Object Detection, Distance Estimation, Computer Vision, OpenCV, Real-Time Systems, Assistive Technology, Deep Learning, Smart Surveillance, Robotics