Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments

TL;DR

This paper compares an ensemble learning model with a large language model for sidewalk detection, demonstrating the ensemble's superior robustness and accuracy in urban environments across various datasets and noise conditions.

Contribution

The study introduces a robust ensemble model that outperforms the state-of-the-art LLM in sidewalk detection, especially under challenging noisy conditions.

Findings

Ensemble model achieved high mIOU scores of over 90% on multiple datasets.

Ensemble maintained performance under noise, while LLM performance declined.

Ensemble outperforms LLM in robustness and reliability for urban sidewalk detection.

Abstract

This study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. Accurate sidewalk detection is crucial in improving road safety and urban planning. The study evaluated the model's performance on Cityscapes, Ade20k, and the Boston Dataset. The results showed that the ensemble model performed better than the individual models, achieving mean Intersection Over Union (mIOU) scores of 93.1\%, 90.3\%, and 90.6\% on these datasets under ideal conditions. Additionally, the ensemble model maintained a consistent level of performance even in challenging conditions such as Salt-and-Pepper and Speckle noise, with only a gradual decrease in efficiency observed. On the other hand, the ONE-PEACE LLM performed slightly better than the ensemble model in ideal scenarios but experienced a significant decline in performance under noisy conditions. These findings demonstrate the robustness and reliability of the ensemble model, making it a valuable asset for improving urban infrastructure related to road safety and curb space management. This study contributes positively to the broader context of urban health and mobility.