Hardware Design for Smart Walking Stick Supporting Neural Networks
P. Devaki1, S. Shivavarsha2, G. Bala Kowsalya3, M. Manjupavithraa4, V. Vijilesh5
1Dr. P. Devaki, Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2S. Shivavarsha, UG Final Year, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
3G. Bala Kowsalya, UG Final Year, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
4M. Manjupavithraa, UG Final Year, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
5V. Vijilesh, Associate Professor, Department of Information Technology, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 07 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 26 December 2019 | PP: 407-410 | Volume-8 Issue-12S October 2019 | Retrieval Number: L110210812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1102.10812S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper aims to bring out the efficient hardware system design to be used in walking stick by the visually impaired people especially to support the cutting edge software technologies to assist in their mobility. It is designed in such a way that it is convenient to handle and also to perform heavier programs without any degradation in accuracy. Hardware design uses Rasberry pi3 Model B for finding the obstacle and to find the distance of the obstacle. Pi camera is used to capture the video frames and feed each frame for processing. For real time object detection, the proposed system uses neural network to train the images.
Keywords: Raspberry Pi3, Pi Camera, Walking Stick, Real Time Object Detection, Neural Network.
Scope of the Article: Smart Spaces