Smart Obstacle Detection System: An AI-IoT Powered Assistive System for the Visually Impaired

Abstract

The millions of people who are visually challenged in the world almost invariably have to grapple with the three problems: navigating an environment, recognizing visual content, and social interaction. The use of traditional aids such as white canes and guide dogs, no matter how helpful, is devoid of real-time contextual awareness. This paper presents the Smart Obstacle Detection System, an innovative AI-IoT integrated assistive system that helps the visually impaired be more independent, safe, and socially interactive. Smart Obstacle Detection System provides real-time feedback to users via voice output by performing obstacle detection, facial recognition, emotion analysis, OCR, and scene description. The system was built using Python modules, namely ESP32-CAM for image capturing, ultrasonic sensors for object detection, and the use of AI models such as YOLO and ConvNeXt for smart interactions. Reversible data hiding was included in the project to ensure privacy and integrity. Smart Obstacle Detection System symbolizes the true potential of AI-IoT fusion in creating accessibility.

Introduction

Over 285 million people worldwide are blind, and while traditional aids like white canes and guide dogs assist with navigation, they do not provide rich environmental or contextual information. The integration of AI and IoT technologies offers new possibilities for enhancing independence and quality of life for the visually impaired.

This paper presents a Smart Obstacle Detection System—a modular AI-IoT assistive device combining obstacle detection, scene description, facial and emotion recognition, and optical character recognition (OCR) for reading text. The system uses hardware such as an ESP32-CAM, ultrasonic sensors, buzzer, and speakers, alongside desktop processing for AI tasks, coordinated through a voice-controlled interface.

Key functional modules include real-time obstacle alerts, natural language scene narration, text-to-speech for reading printed text, facial and emotion recognition via CNN models, and an SOS alert feature for emergencies. The system supports seamless voice interaction and integrates multiple AI models to provide timely, context-aware feedback.

Testing showed high accuracy across tasks: obstacle detection (~95%), OCR (~98% on high-quality images), face and emotion recognition (~93-95%), with responsive voice feedback. User feedback from visually impaired individuals highlighted ease of use and clarity of audio output. Security is addressed via data hiding using LSB steganography, with plans for enhanced cryptographic methods.

Future improvements aim to increase data embedding capacity and security using advanced AI-driven steganography, cryptography, and support for broader media types. Overall, this system exemplifies a scalable, integrated approach to assistive technology for the visually impaired, enhancing navigation, environmental awareness, and communication.

Conclusion

In excess of 285 million persons in the world are reportedly blind by the World Health Organization. People, even in sightedness, can get information about their environments that are needed to live daily. Traditional aids such as white canes and guide dogs provide some assistance; however, they do not assist users in obtaining contextual environmental information, emotional reading of people in the vicinity, or access to written information.

References

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Copyright

Copyright © 2025 Hemamalini M., Manushri Shivaani KS, Sadhana M., Dr. Ezhilarasan M.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.