Citation

Leong, Xinnan and Ramasamy, R. Kanesaraj (2023) Obstacle Detection and Distance Estimation for Visually Impaired People. IEEE Access, 11. pp. 136609-136629. ISSN 2169-3536

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Abstract

In the realm of assistive technologies for visually impaired persons (VIPs), existing solutions such as white canes and guide dogs have limitations in range and practicality. Moreover, current electronic systems often fall short in terms of portability and the ability to estimate distances in real-time. To bridge these gaps, this study introduces a revolutionary wearable device comprising a Raspberry Pi, a camera module, and a pretrained convolutional neural network, all integrated into a pair of smart glasses. These glasses are designed to identify objects and estimate their distances from the wearer, providing real-time auditory or haptic feedback. The development process was rigorous, involving the deployment of machine learning algorithms for object identification and the integration of camera and sensor technology into a lightweight, user-friendly frame. The system’s performance was extensively evaluated using quantitative metrics, showing its precision, speed, and usability. Conclusively, this study presents a significant leap in wearable assistive technologies, offering enhanced spatial awareness, autonomy, and quality of life for VIPs.

Item Type:	Article
Uncontrolled Keywords:	Iot, cameras
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television T Technology > TR Photography > TR250-265 Cameras
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2024 03:02
Last Modified:	03 Jan 2024 03:02
URII:	http://shdl.mmu.edu.my/id/eprint/11991

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