# DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

## TL;DR

This paper introduces DEDUCE, a set of diverse scene detection algorithms that improve visual place recognition in unseen and challenging environments for service robots, reducing reliance on human training.

## Contribution

The paper presents five novel scene detection methods combining deep fusion models, evaluated on multiple datasets and real-world videos, outperforming existing state-of-the-art systems.

## Key findings

- Improved accuracy over existing visual place recognition systems.
- Effective in unseen and challenging environments.
- Validated on diverse datasets and real-world videos.

## Abstract

In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE - Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as real-world videos acquired through multiple mobile platforms. The final results show an improvement over the existing state-of-the-art visual place recognition systems.