Implementation of an IoT-Based Smart Cane Using YOLO V3 to Enhance the Mobility and Safety of Visually Impaired Individuals
DOI:
https://doi.org/10.37859/coscitech.v6i2.9793
Abstract
Visually impaired individuals face significant challenges in mobility and safety during daily activities, especially in public spaces that are not disability-friendly. Conventional white canes are limited in their ability to detect obstacles. This study aims to design and implement a smart cane based on the Internet of Things (IoT) and the real-time object detection algorithm YOLO V3 to enhance the mobility and safety of visually impaired users. The developed system utilizes ultrasonic sensors to detect obstacles on the left and right sides of the user, a GPS module for real-time location tracking via a web server, and an ESP32-CAM integrated with YOLO V3 to detect objects such as vehicles, holes, and people. Information is conveyed to the user through voice alerts using a DFPlayer Mini and is also displayed on an LCD and a web interface. Test results show that the system operates accurately, with an average sensor error rate of only 0.12%, and all components function properly. Usability testing involving 50 respondents indicates a very high level of user satisfaction, with average agreement rates exceeding 85%. This research demonstrates that the integration of IoT and computer vision can produce a smart, responsive, and user-friendly assistive device for the visually impaired.
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