Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms

TL;DR

This paper evaluates four real-time object detection algorithms to determine their suitability for indoor navigation assistance for the visually impaired, focusing on accuracy, speed, and adaptability.

Contribution

It provides a comprehensive performance comparison of YOLO, SSD, Faster R-CNN, and Mask R-CNN specifically for indoor assistive navigation applications.

Findings

YOLO offers the fastest detection speed.

Faster R-CNN achieves higher accuracy.

Trade-offs exist between precision and processing speed.

Abstract

This study addresses the need for accurate and efficient object detection in assistive technologies for visually impaired individuals. We evaluate four real-time object detection algorithms YOLO, SSD, Faster R-CNN, and Mask R-CNN within the context of indoor navigation assistance. Using the Indoor Objects Detection dataset, we analyze detection accuracy, processing speed, and adaptability to indoor environments. Our findings highlight the trade-offs between precision and efficiency, offering insights into selecting optimal algorithms for realtime assistive navigation. This research advances adaptive machine learning applications, enhancing indoor navigation solutions for the visually impaired and promoting accessibility.