Synergistic Perception and Generative Recomposition: A Multi-Agent Orchestration for Expert-Level Building Inspection
Hui Zhong, Yichun Gao, Luyan Liu, Xusen Guo, Zhaonian Kuang, Qiming Zhang, Xinhu Zheng

TL;DR
FacadeFixer is a multi-agent framework that improves building facade defect detection by collaborative perception and generative data augmentation, addressing challenges like data scarcity and defect complexity.
Contribution
It introduces a unified multi-agent system for defect detection and segmentation, coupled with a generative agent for high-quality data synthesis, advancing infrastructure inspection methods.
Findings
Outperforms state-of-the-art detection and segmentation models.
Effectively synthesizes realistic defect data for training.
Enhances defect localization accuracy in complex facade scenarios.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
