Synergistic Perception and Generative Recomposition: A Multi-Agent Orchestration for Expert-Level Building Inspection

• URL: http://arxiv.org/abs/2603.20143v1
• Date: Fri, 20 Mar 2026 17:17:22 GMT
• Title: Synergistic Perception and Generative Recomposition: A Multi-Agent Orchestration for Expert-Level Building Inspection
• Abstract summary: Building facade defect inspection is fundamental to structural health monitoring and sustainable urban maintenance.<n>textitFacadeFixer treats defect perception as a collaborative reasoning task rather than isolated recognition.<n>textitFacadeFixer orchestrates specialized agents for detection and segmentation to handle multi-type defect interference.<n>textitFacadeFixer significantly outperforms state-of-the-art (SOTA) baselines.
• Score: 6.469960133115484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building facade defect inspection is fundamental to structural health monitoring and sustainable urban maintenance, yet it remains a formidable challenge due to extreme geometric variability, low contrast against complex backgrounds, and the inherent complexity of composite defects (e.g., cracks co-occurring with spalling). Such characteristics lead to severe pixel imbalance and feature ambiguity, which, coupled with the critical scarcity of high-quality pixel-level annotations, hinder the generalization of existing detection and segmentation models. To address gaps, we propose \textit{FacadeFixer}, a unified multi-agent framework that treats defect perception as a collaborative reasoning task rather than isolated recognition. Specifically,\textit{FacadeFixer} orchestrates specialized agents for detection and segmentation to handle multi-type defect interference, working in tandem with a generative agent to enable semantic recomposition. This process decouples intricate defects from noisy backgrounds and realistically synthesizes them onto diverse clean textures, generating high-fidelity augmented data with precise expert-level masks. To support this, we introduce a comprehensive multi-task dataset covering six primary facade categories with pixel-level annotations. Extensive experiments demonstrate that \textit{FacadeFixer} significantly outperforms state-of-the-art (SOTA) baselines. Specifically, it excels in capturing pixel-level structural anomalies and highlights generative synthesis as a robust solution to data scarcity in infrastructure inspection. Our code and dataset will be made publicly available.

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