HOMEY: Heuristic Object Masking with Enhanced YOLO for Property Insurance Risk Detection
This addresses the problem of scalable risk detection for property insurance, though it appears incremental as it builds on existing YOLO methods with domain-specific enhancements.
The paper tackled automated property risk detection by introducing HOMEY, a framework that combines YOLO with heuristic object masking and a custom loss function, achieving superior detection accuracy and reliability compared to baseline YOLO models on real-world property imagery.
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.