Robotic Guide Dog for Real-time Indoor Object Detection and Classification with Localization

Abstract

Guide dog robots with advanced sensing abilities could be a big boon to vision-impaired people as some of them may choose technological solutions over real-life guide dogs. In this study, we propose a method that combines a robotic guide dog sensing system with the YOLO-GUIDE framework to enable real-time indoor object detection and classification with localization. The performance was assessed using ten indoor objects. The qualitative test outcomes showed the effectiveness of the proposed method, while quantitative evaluation results with 0.76 Precision, 0.67 Recall, and a 0.71 F1-score indicate high performance. The YOLO-GUIDE proved its superiority by outperforming other relevant models.