Abstract
Doing activities such as navigation is a big problem for people with visual impairment. It makes them inactive and isolates them from communicating with the people around them. A lot of technological interventions have been proposed to solve and overcome these problems. This paper proposes a solution to identify popular objects and avoid obstacles around them. YOLOv3 and Tiny-YOLO3 deep learning models are trained with multiple images containing obstacles that the visually impaired person will face indoors. The results show an average accuracy of 94.6% for object detection while using the YOLOv3 model, and 97.91% recognition accuracy is achieved for using the same model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ackland P, Resnikoff S, Bourne R (2018) World blindness and visual impairment: despite many successes, the problem is growing. Commun Eye Heal J 30:71–73
Elgendy M, Sik-Lanyi C, Kelemen A (2019) Making shopping easy for people with visual impairment using mobile assistive technologies. Appl Sci 9:1061
Manjari K, Verma M, Singal G (2020) A survey on assistive technology for visually impaired. Internet Things. 11
De Silva SA, Dias D (2016) A sensor platform for the visually impaired to walk straight avoiding obstacles. In: Proceedings of the international conference on sensing technology (ICST), pp 838–843
Wachaja A, Agarwal P, Zink M, Adame MR, Möller K, Burgard W (2017) Navigating blind people with walking impairments using a smart walker. Autono Rob 41:555–573
Mekhalfi ML, Melgani F, Zeggada A, De Natale FGB, Salem MA-M, Khamis A (2016) Recovering the sight to blind people in indoor environments with smart technologies. Expert Syst Appl 46:129–138
Aladren A, Lopez-Nicolas G, Puig L, Guerrero JJ (2016) Navigation assistance for the visually impaired using RGB-D sensor with range expansion. IEEE Syst 10:922–932
Liao C-F (2016) An integrated assistive system to support wayfinding and situation awareness for people with vision impairment. ProQuest Dissertion Theses, 291
Mocanu B, Tapu R, Zaharia T (2018) Deep-see face: a mobile face recognition system dedicated to visually impaired people. IEEE Access 6:51975–51985
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788
Soviany P, Ionescu RT (2018) Optimizing the trade-off between single-stage and two-stage deep object detectors using image difficulty prediction. In: IEEE 20th international symposium on symbolic and numeric algorithms for scientific computing (SYNASC), pp. 209–214
Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 6517–6525
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement
Acknowledgements
The authors would like to acknowledge the financial support of Széchenyi 2020 under the EFOP-3.6.1-16-2016-00015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Elgendy, M., Lanyi, C.S. (2022). Helping People with Visual Impairments to Avoid Obstacles Using Deep Learning. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_79
Download citation
DOI: https://doi.org/10.1007/978-981-16-1781-2_79
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1780-5
Online ISBN: 978-981-16-1781-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)