A TinyML model for sidewalk obstacle detection: aiding the blind and visually impaired people

This paper presents a pioneering study on the feasibility of implementing deep learning on resource-restricted IoT devices for real-world applications. We introduce a TinyML model configured for sidewalk obstacle detection tailored explicitly to assist those with visual impairments-a demographic often hindered by urban navigation challenges. Our investigation primarily focuses on adapting traditionally computationally intensive deep learning models to the stringent confines of IoT systems, where both memory and processing power are markedly limited. With a remarkably small footprint of just 1.93 MB and a robust mean average precision (mAP) of 50%, the proposed model achieves breakthrough outcomes, making it particularly well-suited for lightweight IoT devices. We demonstrate an exceptional inference speed of 96.2 milliseconds on a standard CPU, signifying a substantial step toward real-time processing in assistive technologies. The implications of this research are profound, emphasizing TinyML’s potential to bridge the gap between advanced machine learning capabilities and the accessibility demands of assistive devices for visually impaired individuals.