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Research Article
Blind People Assistance for Object Detection Using AI
Surendarkumar S1
Ranjithkumar M2
Deepanraj B3
Sivashankar M4
Umapathy M5
12345 Computer Science and Engineering, The Kavery Engineering College in Mecheri, Tamilnadu, India.
Published Online: May-June 2023
Pages: 222-226
Cite this article
↗ https://www.doi.org/10.59256/ijire.2023040378
References
[1] Girshick, R., Donahue, J., Darrell, T., Malik, J., (2014). Rich feature hierarchies for accurate object detection and semantic
segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580–587.
[2] Glorot, X., Bengio, Y., (2010). Understanding the difficulty of training deep feedforward neural networks., in: Aistats, pp. 249–256.
[3] Goodfellow, I.J., (2013). Piecewise linear multilayer perceptrons and dropout. stat 1050, 22.
[5] He, K., Zhang, X., Ren, S., Sun, J., (2016). Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 770–778.
[6] Jung, K., (2012). Object recognition on mobile devices, in: Consumer Electronics-Berlin (ICCE-Berlin), 2012 IEEE International
Conference on, IEEE. pp. 258–262.
[7] Kingma, D., Ba, J., (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
[8] Krizhevsky, A., Sutskever, I., Hinton, G.E., (2012). Imagenet classification with deep convolutional neural networks, in: Advances in
neural information processing systems, pp. 1097–1105.
[9] Le, Q.V., Karpenko, A., Ngiam, J., Ng, A.Y., (2011). Ica with reconstruction cost for efficient overcomplete feature learning, in:
Advances in Neural Information Processing Systems, pp. 1017–1025.
[10] LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., (1989). Backpropagation applied to
handwritten zip code recognition. Neural computation 1, 541–551.
[11] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., (1998)a. Gradient-based learning applied to document recognition. Proceedings of the
IEEE 86, 2278–2324
segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580–587.
[2] Glorot, X., Bengio, Y., (2010). Understanding the difficulty of training deep feedforward neural networks., in: Aistats, pp. 249–256.
[3] Goodfellow, I.J., (2013). Piecewise linear multilayer perceptrons and dropout. stat 1050, 22.
[5] He, K., Zhang, X., Ren, S., Sun, J., (2016). Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 770–778.
[6] Jung, K., (2012). Object recognition on mobile devices, in: Consumer Electronics-Berlin (ICCE-Berlin), 2012 IEEE International
Conference on, IEEE. pp. 258–262.
[7] Kingma, D., Ba, J., (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
[8] Krizhevsky, A., Sutskever, I., Hinton, G.E., (2012). Imagenet classification with deep convolutional neural networks, in: Advances in
neural information processing systems, pp. 1097–1105.
[9] Le, Q.V., Karpenko, A., Ngiam, J., Ng, A.Y., (2011). Ica with reconstruction cost for efficient overcomplete feature learning, in:
Advances in Neural Information Processing Systems, pp. 1017–1025.
[10] LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., (1989). Backpropagation applied to
handwritten zip code recognition. Neural computation 1, 541–551.
[11] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., (1998)a. Gradient-based learning applied to document recognition. Proceedings of the
IEEE 86, 2278–2324
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