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

2026-03-19

AI Summary

This study addresses the need for automated property risk identification in insurance underwriting by proposing a YOLO-based object detection method capable of efficiently recognizing 17 categories of structural damage, maintenance deficiencies, and safety hazards. The approach introduces a heuristic object masking mechanism to enhance detection of weak-signal targets and incorporates a risk-aware weighted loss function to mitigate challenges arising from class imbalance and varying risk severities. Experimental results on real-world property images demonstrate that the proposed method significantly outperforms baseline models in detection accuracy and reliability while preserving YOLO’s computational efficiency, thereby offering a cost-effective and interpretable solution for property risk assessment in insurance applications.

Technology Category

• Computer Vision: Object Detection & Categorization
• Natural Language Processing: Safety and Robustness
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• property insurance
• risk detection
• object detection
• computer vision
• structural damage

Innovation

• Heuristic Object Masking
• Risk-aware Loss Calibration
• Enhanced YOLO
• Property Insurance Risk Detection
• Class Imbalance Handling