用门检测与大模型结合，自动核对建筑设施数量是否合规。
Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models
- 通过门检测定位空间，结合大模型推理完成设施计数。
- 在真实与合成图纸上均实现高准确率，泛化能力强。
- 适合建筑合规检查、智能审图等工程自动化场景。
建筑合规检查（BCC）是确保建成设施符合法规标准的关键流程。其核心在于准确统计各类设施的数量及其空间分布。尽管重要，该问题在现有文献中长期被忽视，严重制约了BCC效率，造成工作流中的关键空白。人工统计耗时费力。近年来大语言模型（LLMs）的发展为融合视觉识别与推理能力提供了新可能。本文提出一项新任务：建筑合规检查中的自动化设施枚举，即验证各类设施数量是否满足法定要求。为此，我们提出一种创新方法，将门检测与基于大模型的推理相结合，并引入链式思维（CoT）管道进一步提升性能。我们是首个将大模型应用于该任务的研究，且所提方法在多种数据集和设施类型上表现良好。在真实与合成平面图上的实验验证了方法的有效性与鲁棒性。
原文摘要 · Abstract (English)
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.
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