用智能遮罩提升弱信号识别，精准检测17类房产风险
HOMEY: Heuristic Object Masking with Enhanced YOLO for Property Insurance Risk Detection
- 通过启发式对象遮罩增强复杂背景下的微弱风险信号
- 在真实房产图像上准确识别17类风险，优于基础YOLO模型
- 适合保险机构用于低成本、可解释的自动化风险评估
自动化房产风险检测是计算机视觉中高影响力但研究不足的领域，对房地产、承保和保险运营具有直接意义。我们提出HOMEY（启发式对象遮罩增强型YOLO），一种结合特定领域遮罩机制与定制损失函数的新型检测框架。HOMEY旨在检测17类与风险相关的房产类别，包括结构损伤（如地基裂缝、屋顶问题）、维护疏忽（如枯萎草坪、杂草丛生）以及责任隐患（如脱落的排水沟、垃圾堆积、警示标志）。该方法引入启发式对象遮罩以放大复杂背景中的微弱信号，并采用风险感知损失校准来平衡类别偏斜与严重性权重。在真实房产图像上的实验表明，HOMEY在检测精度和可靠性方面均优于基线YOLO模型，同时保持快速推理速度。除检测外，HOMEY还支持可解释且成本低廉的风险分析，为可扩展的AI驱动房产保险工作流程奠定基础。
原文摘要 · Abstract (English)
Automated property risk detection is a high-impact yet underexplored frontier in computer vision with direct implications for real estate, underwriting, and insurance operations. We introduce HOMEY (Heuristic Object Masking with Enhanced YOLO), a novel detection framework that combines YOLO with a domain-specific masking mechanism and a custom-designed loss function. HOMEY is trained to detect 17 risk-related property classes, including structural damages (e.g., cracked foundations, roof issues), maintenance neglect (e.g., dead yards, overgrown bushes), and liability hazards (e.g., falling gutters, garbage, hazard signs). Our approach introduces heuristic object masking to amplify weak signals in cluttered backgrounds and risk-aware loss calibration to balance class skew and severity weighting. Experiments on real-world property imagery demonstrate that HOMEY achieves superior detection accuracy and reliability compared to baseline YOLO models, while retaining fast inference. Beyond detection, HOMEY enables interpretable and cost-efficient risk analysis, laying the foundation for scalable AI-driven property insurance workflows.
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