Current - Issue
Original Article
Real-time colour detection and audio feedback using AI- enabled smart spectacles for visually challenged people
Dr.J. Suganthi Vinodhini1
1 Assistant Professor, Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 646-656
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502075References
1. D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, 2nd ed. Upper Saddle River, NJ, USA: Pearson, 2012.
2. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. Cambridge, U.K.: Cambridge University Press, 2004.
3. S. Joseph, R. Lakshmanan, and K. Balakrishnan, "Wearable assistive device for visually impaired people using computer vision and speech
feedback," in Proc. IEEE Int. Conf. Communication and Signal Processing (ICCSP), Chennai, India, 2020, pp. 1156–1160.
4. A. Kumar and P. Sharma, "Real-time object and color recognition system for visually impaired users," in Proc. IEEE Int. Conf. Intelligent
Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 842–847.
5. M. Bousbia-Salah, M. Fezari, A. Chibani, and Y. Amirat, "A smart navigation system for visually impaired people," in Proc. IEEE Int. Conf.
Systems, Man and Cybernetics (SMC), Montreal, QC, Canada, 2007, pp. 1003–1008.
6. [13] J. C. Neto, H. A. F. Rocha, H. F. Morais, A. T. C. Campos, and V. M. F. Almeida, "An assistive navigation system for visually impaired
using computer vision," in Proc. IEEE Int. Conf. Systems, Signals and Image Processing (IWSSIP), 2018, pp. 1–5.
7. [14] A. Aladren, G. Lopez-Nicolas, L. Puig, and J. J. Guerrero, "Navigation assistance for the visually impaired using RGB-D sensors and
computer vision," IEEE Trans. Cybern., vol. 46, no. 3, pp. 584–596, Mar. 2016.
8. [15] S. Mascetti, D. Ahmetovic, A. Gerino, and C. Bernareggi, "Robust traffic lights detection on mobile devices for visually impaired
pedestrians," Comput. Vis. Image Understand., vol. 148, pp. 123–135, Jul. 2016.
9. [16] A. Rodriguez, J. Yebes, P. Alcantarilla, L. Bergasa, J. Almazan, and A. Cela, "Assistive technology for visually impaired people based
on image processing and audio feedback," in Proc. IEEE Intelligent Vehicles Symposium (IV), 2012, pp. 1038–1043.
10. [17] N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–
66, Jan. 1979.
11. [18] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.
12. [19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Advances
in Neural Information Processing Systems (NIPS), 2012, pp. 1097–1105.
13. [20] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
2. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. Cambridge, U.K.: Cambridge University Press, 2004.
3. S. Joseph, R. Lakshmanan, and K. Balakrishnan, "Wearable assistive device for visually impaired people using computer vision and speech
feedback," in Proc. IEEE Int. Conf. Communication and Signal Processing (ICCSP), Chennai, India, 2020, pp. 1156–1160.
4. A. Kumar and P. Sharma, "Real-time object and color recognition system for visually impaired users," in Proc. IEEE Int. Conf. Intelligent
Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 842–847.
5. M. Bousbia-Salah, M. Fezari, A. Chibani, and Y. Amirat, "A smart navigation system for visually impaired people," in Proc. IEEE Int. Conf.
Systems, Man and Cybernetics (SMC), Montreal, QC, Canada, 2007, pp. 1003–1008.
6. [13] J. C. Neto, H. A. F. Rocha, H. F. Morais, A. T. C. Campos, and V. M. F. Almeida, "An assistive navigation system for visually impaired
using computer vision," in Proc. IEEE Int. Conf. Systems, Signals and Image Processing (IWSSIP), 2018, pp. 1–5.
7. [14] A. Aladren, G. Lopez-Nicolas, L. Puig, and J. J. Guerrero, "Navigation assistance for the visually impaired using RGB-D sensors and
computer vision," IEEE Trans. Cybern., vol. 46, no. 3, pp. 584–596, Mar. 2016.
8. [15] S. Mascetti, D. Ahmetovic, A. Gerino, and C. Bernareggi, "Robust traffic lights detection on mobile devices for visually impaired
pedestrians," Comput. Vis. Image Understand., vol. 148, pp. 123–135, Jul. 2016.
9. [16] A. Rodriguez, J. Yebes, P. Alcantarilla, L. Bergasa, J. Almazan, and A. Cela, "Assistive technology for visually impaired people based
on image processing and audio feedback," in Proc. IEEE Intelligent Vehicles Symposium (IV), 2012, pp. 1038–1043.
10. [17] N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–
66, Jan. 1979.
11. [18] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.
12. [19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Advances
in Neural Information Processing Systems (NIPS), 2012, pp. 1097–1105.
13. [20] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026