Robotic guide dog for real-time indoor object detection and classification with localization

Nathan Rees, Karthick Thiyagarajan, Sarath Kodagoda

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the IEEE Applied Sensing Conference (APSCON 2024), 22-24 January 2024, Goa, India
PublisherIEEE
Number of pages4
ISBN (Print)9798350317275
DOIs
Publication statusPublished - 2024
EventIEEE Applied Sensing Conference -
Duration: 1 Jan 2024 → …

Conference

ConferenceIEEE Applied Sensing Conference
Period1/01/24 → …

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