HOMEY: Heuristic Object Masking with Enhanced YOLO for Property Insurance Risk Detection

Abstract

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.

Figures (12)

• Figure 1: HOMEY architecture and performance highlights. We propose HOMEY, a novel property risk detection framework built upon YOLO and enhanced with heuristic masking strategies and a custom loss design. HOMEY effectively identifies 17 distinct classes of property risks --- ranging from cracked foundations and roof issues to overgrown yards and hazardous structures. The framework introduces domain-informed masking and tailored loss calibration, enabling robust detection in noisy, real-world residential imagery. By bridging high-precision computer vision with property insurance needs, HOMEY provides a scalable and interpretable pathway for automated property risk assessment.
• Figure 2: Sample property detection results on two real-world images. Each row shows: (1) original input image, (2) ground-truth labels, (3) baseline YOLO detection, and (4) our proposed HOMEY predictions. HOMEY demonstrates superior localization and classification of property risk elements, particularly in cluttered scenes.
• Figure 3: Additional property detection examples showcasing the robustness of HOMEY. As in Figure \ref{['fig:HOMEY_sample_02']}, each row contains original image, ground truth, baseline detection, and HOMEY output. Notice how HOMEY consistently captures subtle damages, overgrowth, and risk objects that the baseline model often misses.
• Figure 4: Training dynamics of the baseline: total loss and bounding-box loss.
• Figure 5: Training dynamics of HOMEY: total loss and bounding-box loss. HOMEY converges faster and achieves lower loss values across epochs.