AI Navigo: Innovative Wearable for Visual Assistance

Abstract

Assistive navigation for visually impaired users requires reliable obstacle detection on portable, low-cost hardware. We present AI Navigo, a wearable obstacle-awareness system that combines monocular video, YOLO-based object detection, and audio feedback delivered through Bluetooth earbuds. The system is implemented on a Raspberry Pi with an endoscope camera mounted on eyeglasses and it is designed to detect four outdoor obstacle classes relevant to pedestrian safety: (a) cars, (b) persons, (c) traffic cones, and (d) potholes. We evaluate YOLOv9 and YOLOv10 on a dataset of 6,276 images collected from outdoor pedestrian environments and compare their performance under multiple learning-rate and epoch settings. YOLOv9 achieved the strongest overall results, reaching a detection score of 0.847 and outperforming YOLOv10 in difficult classes such as potholes and persons, while also showing more stable validation behavior and better generalization. The final deployment pipeline converts detections into spatial audio descriptions using a lightweight coordinate-to-description mapping function. Although inference speed on the Raspberry Pi remains a practical limitation, the results demonstrate the feasibility of low-cost vision-based obstacle awareness for wearable navigation assistance. Future work will focus on improving runtime efficiency, expanding obstacle coverage, and evaluating the system in real user settings.