Deep Learning-Based Assistive System for Visually Impaired Individuals: A Comparative Study of YOLO Models

Author(s)	Ms. Dhanya Raju, Ms. Anitha Krishnan G
Country	India
Abstract	Navigating safely and independently remains a major challenge for individuals with visual impairments. In this paper, we present an innovative assistive solution based on deep learning, which uses object detection and audio cues to improve mobility. It incorporates various implementations of the YOLO (You Only Look Once) algorithm, designed for use on mobile devices, embedded platforms, and live video processing, object recognition, and audio notifications. A detailed comparison will look at YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv11 algorithms. Accuracy, speed, efficiency, and practicality will be emphasized. From experiments conducted in different environments to actual applications, YOLOv4 and YOLOv8 have proven themselves to be the best algorithms in embedding and accuracy, respectively.
Keywords	YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv11, Object Detection, Deep Learning, Assistive Technology, Auditory Feedback, Visual Impairment, Real-Time Navigation, Accessibility, Artificial Intelligence.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-05-05

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