Empirical Evaluation of a Fully Offline, Low-Cost Edge AI Wearable Smart Navigation Assistant for Distance-Aware Obstacle Detection

Abstract

Real-time visual perception on resource-constrained embedded hardware must reconcile computational economy, low latency, and dependable sensing accuracy within tight power and cost envelopes. This paper reports on the Smart Navigation Device (SND), a wearable assistive perception system that performs object detection, distance ranging, sensor fusion, and speech feedback entirely on a Raspberry Pi 4B without any cloud dependency. At the core of the system is the Cascaded Detection-Ranging Fusion (CDRF) framework, a four-stage pipeline that couples the lightweight YOLO11n detector with ultrasonic time-of-flight ranging through confidence-guided detection acceptance, spatial-zone partitioning, dominant-object association, and adaptive suppression of redundant announcements. The hardware-software co-design keeps every processing stage — image capture, neural inference, ranging, fusion, and text-to-speech synthesis — local to the device, eliminating transmission latency, removing a major privacy exposure, and preserving operability where network connectivity is unreliable or absent. The framework was evaluated across 169 controlled trials spanning three obstacle categories — person, chair, and laptop — at distances from 0.30 m to 4.20 m. Detection rates of 90.4%, 92.9%, and 77.0% were obtained for the three classes respectively, with mean absolute ranging errors of 2.11 cm, 1.60 cm, and 1.79 cm. Agreement between the ultrasonic estimate and ground-truth distance was excellent (Pearson r = 0.9995, p < 10⁻²¹⁹), and Bland–Altman analysis revealed a small systematic bias of −1.10 cm (95% limits of agreement: −7.88 cm to 5.68 cm). A chi-square test indicated a statistically meaningful distance-dependent decline in laptop-class detection reliability. Benchmarked against previously reported wearable travel aids, the SND achieves comparable or better detection reliability than low-cost ultrasonic-only alternatives while additionally providing object identity, and does so at a fraction of the hardware cost and without any of the connectivity dependencies of cloud-assisted alternatives — positioning it as a reproducible, statistically grounded, and economically accessible baseline for future assistive-perception research.