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

-- This study addresses the critical need for accurate and efficient object detection in assistive technologies for visually impaired individuals. We systematically evaluate the performance of four prominent real-time object detection algorithms--YOLO, SSD, Faster R-CNN, and Mask R-CNN--within the context of indoor navigation assistance. Our analysis, conducted on the Indoor Objects Detection dataset, focuses on key parameters including detection accuracy, processing speed, and adaptability to the unique challenges of indoor environments. This research contributes to a deeper understanding of adaptive machine learning applications that can significantly improve indoor navigation solutions for the visually impaired, promoting inclusivity and accessibility. In today's technology-driven society, there is an increasing emphasis on enhancing accessibility for visually impaired individuals.