Arseam

International Journal of Advances in Engineering & Scientific Research

ML-POWERED VISION ASSISTANCE SYSTEM FOR THE BLIND AND VISUALLY IMPAIRED TO EMPOWERING INDEPENDENCE AND NAVIGATION

Mr.R.Premkumar, Dinakar C, Gurusanjay S, Sabareeshwaran S

Mr.R.Premkumar, Dinakar C, Gurusanjay S, Sabareeshwaran S, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Tamilnadu, India

DOI : https://doi.org/10.5281/zenodo.14921698 Page No : 12-23

Published Online : 2025-02-25

Download Full Article : PDF Check for Updates

Blindness is one of the most prevalent and challenging disabilities, affecting millions worldwide. To enhance mobility and independence, we propose an ML-powered Vision Assistance System designed for real-time obstacle detection, avoidance, navigation, and position tracking. The system integrates computer vision and deep learning techniques to assist visually impaired individuals in perceiving their surroundings. A camera-based visual detection hybrid is employed to operate effectively even in low-light conditions, leveraging Tensor Flow and pre-trained models for object recognition and scene understanding. The system identifies obstacles, estimates depth to calculate a safe distance, and converts visual information into audio cues, enabling users to navigate both indoors and outdoors with confidence. This approach ensures accessibility through a cost-effective, practical, and reliable solution, significantly improving the quality of life for visually impaired individuals. Additionally, the system is designed to be cost-effective, lightweight, and easy to use, making it an accessible and practical solution for real-world applications. Unlike traditional mobility aids such as walking canes, this vision-based assistance tool offers a comprehensive and intelligent navigation system that enhances the quality of life for the visually impaired. The combination of machine learning, computer vision, speech synthesis, and depth perception makes this approach a reliable, efficient, and user-friendly solution for overcoming mobility challenges faced by blind individuals.

Keywords: ML, Computer Vision, Obstacle Detection, Depth Estimation, Navigation Assistance and Speech Processing

References

Avacharmal, R., Pamulaparthyvenkata, S., Ranjan, P., Mulukuntla, S., Balakrishnan, A., Preethi, P., & Gomathi, R. D. (2024, June). Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484.
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520.
Siddique R, Nivedhitha MS, Jacob B.(2019) Quantitative analysis for detection of toxic elements in various irrigants, their combination (precipitate), and para-chloroaniline: An inductively coupled plasma mass spectrometry study. J Conserv Dent. 2019 Jul-Aug;22(4):344-350. doi: 10.4103/JCD.JCD_95_19.
Barma MD, Muthupandiyan I, Samuel SR, (2021). Amaechi BT. Inhibition of Streptococcus mutans, antioxidant property and cytotoxicity of novel nano-zinc oxide varnish. Arch Oral Biol. 2021 Jun;126:105132. doi: 10.1016/j.archoralbio.2021.105132. Epub 2021 Apr 23.
Patel, P., Pampaniya, S., Ghosh, A., Raj, R., Karuppaih, D., & Kandasamy, S. (2025). Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies. IEEE Access.
Bajaj, R., Khanna, P., Goel, R., & Bhargav, R. (2023, July). Empowering the Visually Impaired: A Sustainable ML-Based Currency Recognition System. In International Conference on Sustainable Development through Machine Learning, AI and IoT (pp. 39-50). Cham: Springer Nature Switzerland.
Preethi, C., Haripriya, K., Hammed, S. S., Pavalarajan, S., Priyadharshini, S., & Achithra, B. V. (2023, December). Reading Technology for Blind People Using OCR and Neura Machine Learning Method. In 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC) (pp. 112-119). IEEE.
Xiao, J., Joseph, S. L., Zhang, X., Li, B., Li, X., & Zhang, J. (2015). An assistive navigation framework for the visually impaired. IEEE transactions on human-machine systems, 45(5), 635-640.
Hakobyan, L., Lumsden, J., O’Sullivan, D., & Bartlett, H. (2013). Mobile assistive technologies for the visually impaired. Survey of ophthalmology, 58(6), 513-528.

Premkumar R, Dinakar C, Gurusanjay S, Sabareeshwaran S (2025), “Ml-Powered Vision Assistance System For The Blind And Visually Impaired To Empowering Independence And Navigation”, International Journal of Advances in Engineering & Scientific Research, Volume 12, Issue 1, 2025, pp 12-23 DOI : https://doi.org/10.5281/zenodo.14921698

Article View: 54
PDF Download: 4

References

Avacharmal, R., Pamulaparthyvenkata, S., Ranjan, P., Mulukuntla, S., Balakrishnan, A., Preethi, P., & Gomathi, R. D. (2024, June). Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484.
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520.
Siddique R, Nivedhitha MS, Jacob B.(2019) Quantitative analysis for detection of toxic elements in various irrigants, their combination (precipitate), and para-chloroaniline: An inductively coupled plasma mass spectrometry study. J Conserv Dent. 2019 Jul-Aug;22(4):344-350. doi: 10.4103/JCD.JCD_95_19.
Barma MD, Muthupandiyan I, Samuel SR, (2021). Amaechi BT. Inhibition of Streptococcus mutans, antioxidant property and cytotoxicity of novel nano-zinc oxide varnish. Arch Oral Biol. 2021 Jun;126:105132. doi: 10.1016/j.archoralbio.2021.105132. Epub 2021 Apr 23.
Patel, P., Pampaniya, S., Ghosh, A., Raj, R., Karuppaih, D., & Kandasamy, S. (2025). Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies. IEEE Access.
Bajaj, R., Khanna, P., Goel, R., & Bhargav, R. (2023, July). Empowering the Visually Impaired: A Sustainable ML-Based Currency Recognition System. In International Conference on Sustainable Development through Machine Learning, AI and IoT (pp. 39-50). Cham: Springer Nature Switzerland.
Preethi, C., Haripriya, K., Hammed, S. S., Pavalarajan, S., Priyadharshini, S., & Achithra, B. V. (2023, December). Reading Technology for Blind People Using OCR and Neura Machine Learning Method. In 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC) (pp. 112-119). IEEE.
Xiao, J., Joseph, S. L., Zhang, X., Li, B., Li, X., & Zhang, J. (2015). An assistive navigation framework for the visually impaired. IEEE transactions on human-machine systems, 45(5), 635-640.
Hakobyan, L., Lumsden, J., O’Sullivan, D., & Bartlett, H. (2013). Mobile assistive technologies for the visually impaired. Survey of ophthalmology, 58(6), 513-528.

Submission Instruction

Call for Paper

Submit Paper Online

Join Editorial Board

Author Guidelines

Impact Factor: 6.013

Indexed in

ML-POWERED VISION ASSISTANCE SYSTEM FOR THE BLIND AND VISUALLY IMPAIRED TO EMPOWERING INDEPENDENCE AND NAVIGATION

Aims and Objectives

Ethics Policy

Peer Review Policy

Call for Paper

Conference

Instruction To Review

Guide To Authors