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

Source: [Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms](<https://www.sota2.com/research/paper/68fa76d51e209131cea6f6a9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-28T05:01:13.271Z

Authors: Abhinav Pratap, Sushant Kumar, Suchinton Chakravarty

Published: 2025-01-30T15:56:20.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.18444>)

[PDF](<https://arxiv.org/pdf/2501.18444.pdf>)