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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63336
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Department of Computer Science and Engineering
D. Y. Patil College of Engineering and Technology, Kolhapur
Navigation in unfamiliar environments is one of the most challenging tasks for visually impaired individuals. While traditional mobility aids such as white canes and guide dogs help detect nearby obstacles, they cannot provide detailed environmental awareness or detect objects beyond ground level. Recent advances in Artificial Intelligence (AI) and Computer Vision have enabled the development of intelligent assistive technologies capable of understanding visual scenes and providing contextual feedback. Smartphones equipped with cameras and powerful processors offer a cost-effective platform for implementing such solutions. The Drishti – Vision for the Blind system is designed as an AI-based mobile application that assists visually impaired users in safely navigating their surroundings. The application processes real-time video captured by a smartphone camera, detects nearby obstacles, estimates their distance, and provides voice instructions to guide the user. The system integrates YOLO-based object detection, Whisper-based voice command recognition, OCR for text reading, and a Hybrid Brain module, and text-to-speech feedback to convert visual data and voice queries into meaningful audio guidance. The backend Vision Consumer layer coordinates Video Frames, Audio Commands, and a Controller to orchestrate the AI Engine pipeline. This approach provides a portable and affordable assistive navigation solution for visually impaired individuals.
KEYWORDS
Drishti – Vision for the Blind presents an AI-driven assistive navigation system designed to improve mobility and independence for visually impaired individuals. By combining real-time object detection (YOLOv8), voice command recognition (Whisper), scene understanding, text reading (OCR), and contextual reasoning via a Hybrid Brain powered by Gemini 2.5 Flash-Lite, Moondream2, and Florence-2, the system delivers rich spoken environmental awareness through a TTS Output pipeline. The Vision Consumer backend layer ensures smooth coordination between video frame ingestion, audio command processing, and the AI Engine, making the overall system responsive and scalable. The results demonstrate that the proposed system can effectively detect obstacles and provide timely audio guidance. With further improvements, Drishti has the potential to become a widely adopted assistive technology for visually impaired users.
[1] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788. [2] G. Jocher et al., “Ultralytics YOLOv8,” GitHub Repository, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics [3] A. Radford et al., “Robust Speech Recognition via Large-Scale Weak Supervision,” in Proceedings of the 40th International Conference on Machine Learning (ICML), Honolulu, HI, USA, 2023, pp. 28492–28518. [4] Google DeepMind, “Gemini 2.5 Flash-Lite: Efficient Multimodal Reasoning,” Google AI, 2025. [Online]. Available: https://deepmind.google [5] X. Xiao et al., “Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks,” Microsoft Research, 2024. [Online]. Available: https://www.microsoft.com/en-us/research/ [6] V. Kumar, “Moondream2: Lightweight Visual Question Answering Model,” 2024. [Online]. Available: https://github.com/vikhyat/moondream2
Publication Date
5/16/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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