Abstract
In the world, there are around 300 million people (“World Sight Day 2017:” Available: https://www.indiatoday.in/education-today/gk-current-affairs/story/world-sight-day-2017-facts-and-figures-1063009–2017-10–12 (2017) [“World Sight Day 2017:” Available: https://www.indiatoday.in/education-today/gk-current-affairs/story/world-sight-day-2017-facts-and-figures-1063009-2017-10-12 (2017)]), who have problems with vision, among those 40 million are completely blind and around 260 million have poor vision due to some cases being partial or complete visual disability. And among them around 95 percent of these stay in developed countries, where they find it very difficult to perform basic day-to-day activities like commuting. They are unable to read traffic warning signs and regulatory signs, also they cannot read the information signs to know their exact position and they often rely on other pedestrians to guide them to their destination. This proposed model aims to provide a method to solve this issue through an application that contains an image recognition system that detects nearby objects in surroundings. It makes the lives of visually impaired better by making them independent.
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References
“World Sight Day 2017:” Available: https://www.indiatoday.in/education-today/gk-current-affairs/story/world-sight-day-2017-facts-and-figures-1063009-2017-10-12(2017)
P. Dollar, R. Appel, S. Belongie et al., Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
P. Dollar, Z. Tu, P . Perona, et al., Integral Channel Features. British Machine Vision Conference, BMVC, pp. 1–11 (2009)
Y, Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. Comput. Learn. Theory, pp. 23–27 (1995)
D.G. Lowe, Object recognition from local scale-invariant features: International Conference on Computer Vision, pp. 1150–1157 (19999)
M.A. Hearst, S.T. Dumais, E. Osman et al., Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998)
H. Jabnoun, F. Benzarti, H. Amiri, Object detection and identification for blind people in video scene: Université de Tunis El Manar, Ecole Nationale d’Ingénieur de Tunis, In Proc. ResearchGate (Dec 2015)
A. Noorithaya, K. Kumar M, Dr. A. Sreedevi, Voice Assisted Navigation System for the Blind : R.V.C.E , Bangalore, India, In Proc. International Conference on Circuits, communication, control and computing (2014)
A. Nishajith, J. Nivedha, S.S. Nair, Prof. J.M. shaffi, Smart Cap—Wearable Visual Guidance System For Blind, In : Proc. International Conference on Inventive Research on Computing Applications (2018)
H. Park, H. Won, S. Ou, J. Lee (2019) Implementation of crosswalk lights recognition system for the blind's safety: Signal, Images et Technologies de l’Information (LR-SITI-ENIT), IEEE Eurasia Conference on IOT, Communication and Engineering (2019)
R.K. Rakshana, C. Chitra, A smart navguide system for visually impaired. Int. J. Innovative Technol. Explor. Eng. (IJITEE) ISSN: 2278–3075, 8(6S3) (Apr 2019)
Dr. B. Muthusenthil, J. Joshuva, S. Kishore, K. Narendiran: Smart Assistance for Blind People using Raspberry Pi: International Journal of Advance Research, Ideas and Innovations in Technology, ISSN: 2454–132X, Volume 4, Issue 2, (2018)
J. Redmon, S. Divvala, R. Girshick, A. Farhadi: You Only Look Once:Unified, Real-Time Object Detection: IEEE Conference on Computer Vision and Pattern Recognition. (2016). https://doi.org/10.1109/CVPR.2016.91.
Joseph Chet Redmon: YOLO: Real-Time Object Detection: Available : https://pjreddie.com/darknet/yolo/
C. Liu, Y. Tao, J. Liang, K. Li, Y. Chen: Object Detection Based on YOLO Network: IEEE 4th Information Technology and Mechatronics Engineering Conference. https://doi.org/10.1109/itoec.2018.8740604 (2018)
NanoDano: Text-to-speech in Python with pyttsx3 2018, Avaiable: https://www.devdungeon.com/content/text-speech-python-pyttsx3
Z.Q. Zhao, P. Zheng, S.T. Xu, X. Wu:Object Detection with Deep Learning: A Review. In: Proc. arXiv:1807.05511v2 [cs.CV] (2019)
J. Redmon, S. Divvala, R. Girshick: You Only Look Once:Unified, Real-Time Object Detection. In : Proc. arXiv:1506.02640v5 (2016).
H. Yang, H. Wu, H. Chen: Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes, In: Proc. arXiv:1908.05217v1 [cs.CV] (2019) .
M.M. Kamal, A.I. Bayazid, M.S. Sadi, M.M. Islam, N. Hasan: Towards developing walking assistants for the visually impaired people , In: Proceedings IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, (2017), pp. 238–241.
S. Haque, M.S. Sadi, M.E.H Rafi, M.M Islam, M.K Hasan: Real-time crowd detection to prevent stampede: Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems, pp. 665–678. https://doi.org/10.1007/978-981-13-7564-4_56, (2020).
Y. Bengio, Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
J. Bai, S. Lian, Z. Liu, K. Wang, D. Liu, Virtual-blind-road following-based wearable navigation device for blind people: IEEE Trans. Consum. Electron. 64(1), 136–143 (2018)
W. Elmannai, K. Elleithy: Sensor-based assistive devices for visually impaired people: current status, challenges, and future directions :Sensors 17(3), 565–606 (2017).
V. Bansal, K. Balasubramanian, & Natarajan: Obstacle avoidance using stereo vision and depth maps for visual aid devices: SN Appl. Sci. 2, 1131 (2020). https://doi.org/10.1007/s42452-020-2815-z
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Srikanteswara, R., Reddy, M.C., Himateja, M., Kumar, K.M. (2022). Object Detection and Voice Guidance for the Visually Impaired Using a Smart App. In: Shetty D., P., Shetty, S. (eds) Recent Advances in Artificial Intelligence and Data Engineering. Advances in Intelligent Systems and Computing, vol 1386. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_11
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