International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Learning Driven AR Navigation Assistant for Visually Impaired People with Real-Time Audio-Haptic Feedback |
|---|---|
| ABSTRACT | People with visual impairments often face difficulties while navigating unfamiliar indoor and outdoor environments due to obstacles, changing surroundings, and limited awareness of nearby hazards. To address these challenges, this paper presents a Deep Learning Driven Augmented Reality (AR) Navigation System with Audio-Haptic Feedback that provides real-time navigation assistance and supports independent mobility. The proposed system uses YOLOv8 (You Only Look Once Version 8) for object detection and MiDaS (Mixed Datasets for Monocular Depth Estimation) for depth estimation to identify pedestrians, vehicles, furniture, obstacles, and other objects in the user's surroundings. Live video frames captured through a mobile device camera are continuously processed using a FastAPI backend to analyse the environment and determine safe movement directions based on obstacle location and distance. To improve accessibility, the system provides voice guidance in English, Hindi, and Telugu, along with vibration-based haptic alerts that help users respond to nearby obstacles and potentially unsafe situations. AR overlays such as bounding boxes and directional indicators are displayed on the screen to highlight detected objects and navigation regions, making the system useful for partially sighted users and system monitoring. Temporal smoothing techniques are applied to improve the consistency of navigation guidance and reduce unstable decisions during movement. Experimental evaluation demonstrated reliable obstacle detection and effective navigation decision generation under different environmental conditions. By combining deep learning, computer vision, speech interaction, and multimodal feedback, the proposed system offers a practical, affordable, and user-friendly assistive solution that helps visually impaired users navigate more safely, confidently, and independently. |
| AUTHOR | JOGU MOUNIKA, G. NARASIMHAM Post Graduate Student, M. Tech Data Science, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India Associate Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406056 |
| pdf/56_Deep Learning Driven AR Navigation Assistant for Visually Impaired People with Real-Time Audio-Haptic Feedback.pdf | |
| KEYWORDS | |
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