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.

本研究旨在比较强大的集成模型与最先进的 ONE-PEACE 大型语言模型（LLM）在准确检测人行道方面的效果，准确的人行道检测对于提高道路安全和城市规划至关重要。研究评估了模型在 Cityscapes、Ade20k 和波士顿数据集上的性能，结果显示集成模型在理想条件下的平均交集联合（mIOU）得分分别为 93.1％、90.3％和 90.6％，在理想条件下集成模型略优于 ONE-PEACE LLM，但在嘈杂条件下性能显著下降，而集成模型保持了一致的性能水平，仅观察到渐进性的效率下降。这些发现证明了集成模型的稳健性和可靠性，使其成为改善与道路安全和路缘空间管理相关的城市基础设施的宝贵资产。该研究对城市健康和流动性的广泛背景有积极贡献。