A Low-Cost Wearable Visual Aid for Visually Impaired Individuals: Design, Implementation and Evaluation

Abstract

Visual impairment, which affects over 2.2 billion people worldwide, significantly hinders independent navigation and environmental awareness. This paper presents the design and implementation of a low-cost, wearable visual aid that improves navigation safety for visually impaired individuals through real-time object detection and intelligent audio feedback. The system addresses critical gaps in affordability, offline capability, and intelligent information prioritization as a practical alternative to costly commercial assistive devices. The system integrates a Microsoft LifeCam HD-3000 camera with a Raspberry Pi 5 single-board computer running the YOLO-Fastest object detection model using the NCNN inference framework, employing a four-stage pre-processing pipeline and a spatial processing and prioritization algorithm based on monocular distance estimation, directional zoning, trajectory analysis, and hazard level classification. Audio feedback is delivered using the pyttsx3 text-to-speech engine with priority-based speech rate modulation (150–180 wpm) and an interrupt mechanism for critical warnings. Experimental results demonstrated real-time performance with average response times of 55.68–65.18 ms per frame (15–18 FPS) and confidence scores of 0.68–0.85. The system achieved effective detection ranges of 8 m for pedestrians, 33.21 m for static obstacles, and 112.9 m for vehicles. Object prioritization accuracy reached 100% in simple scenarios, degrading to 73.8% in complex environments. Environmental testing revealed detection rates of 95–100% under daylight, decreasing to 30% in near-dark conditions. The prototype, assembled at an affordable price, demonstrates the feasibility of an affordable, edge-deployed wearable visual aid suitable for daytime navigation assistance