基于门检测和大语言模型的建筑合规性检查自动化设施枚举
Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models
AI总结：
本文提出一种结合门检测与大语言模型推理的自动化设施枚举方法，用于建筑合规性检查，通过思维链流程提升性能，并在真实与合成数据上验证了有效性与鲁棒性。
摘要：
建筑合规性检查（BCC）是确保已建成的设施符合监管标准的关键过程。BCC 的核心组成部分之一是准确枚举设施类型及其空间分布。尽管这一问题至关重要，但在现有文献中却很大程度上被忽视，这给 BCC 带来了重大挑战，并在现有工作流程中留下了一个关键缺口。手动执行此任务既耗时又费力。大语言模型（LLMs）的最新进展通过将视觉识别与推理能力相结合，为增强自动化提供了新的机遇。在本文中，我们为 BCC 引入了一项新任务：自动化设施枚举，该任务涉及根据法定要求验证每种设施类型的数量。为了解决这一问题，我们提出了一种将门检测与基于 LLM 的推理相结合的新方法。我们是首个将 LLMs 应用于此任务的研究团队，并进一步通过思维链（CoT）流程提升其性能。我们的方法在多种数据集和设施类型上具有良好的泛化能力。在真实世界和合成平面图数据上的实验证明了我们方法的有效性和鲁棒性。
英文摘要：
Building compliance checking (BCC) is a critical process for ensuring that constructed facilities meet regulatory standards. A core component of BCC is the accurate enumeration of facility types and their spatial distribution. Despite its importance, this problem has been largely overlooked in the literature, posing a significant challenge for BCC and leaving a critical gap in existing workflows. Performing this task manually is time-consuming and labor-intensive. Recent advances in large language models (LLMs) offer new opportunities to enhance automation by combining visual recognition with reasoning capabilities. In this paper, we introduce a new task for BCC: automated facility enumeration, which involves validating the quantity of each facility type against statutory requirements. To address it, we propose a novel method that integrates door detection with LLM-based reasoning. We are the first to apply LLMs to this task and further enhance their performance through a Chain-of-Thought (CoT) pipeline. Our approach generalizes well across diverse datasets and facility types. Experiments on both real-world and synthetic floor plan data demonstrate the effectiveness and robustness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
