AR Guided Inspection System for Property Damage Detection
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Solution Overview
Problem
Current inspection methods for rental properties are inefficient and often require non-professionals to manually document damage, leading to confusion and inaccuracies, especially when dealing with obscured or hard-to-reach areas.
Innovation Solution
A guided inspection system using augmented reality and machine learning that directs users through a physical space, captures images, analyzes them for damage, and provides navigation instructions to ensure thorough and accurate documentation of property conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection forms are used by non-professionals, then inspection coverage can be achieved, but accuracy and reliability of damage detection deteriorate due to confusion and tedium
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated computer vision system using machine learning algorithms. The system automatically captures images, detects damage, and generates inspection reports, eliminating the need for non-professionals to manually examine and document property conditions. This substitution of mechanical human inspection with automated optical and computational systems directly resolves the contradiction by maintaining high productivity while dramatically improving measurement precision through algorithmic objectivity and consistency.
Solution Approach 2:
The inspection system performs self-service by autonomously navigating the property, capturing images, analyzing damage without human intervention, and generating reports. The machine learning model independently identifies and classifies damage types, allowing the system to serve itself in completing the entire inspection workflow without requiring skilled human operators, thus maintaining both high productivity and accurate damage detection.
2Reliability
If comprehensive inspection of all areas is performed manually, then complete damage documentation is achieved, but time consumption increases due to the long list of areas to examine
Solution Approach 1:
The automated inspection system operates continuously without interruption, systematically capturing images of all property areas in sequence. The machine learning model continuously processes images as they are captured, enabling real-time damage detection throughout the inspection. This continuous automated operation ensures complete coverage of all areas while significantly reducing total inspection time compared to manual methods, as the system does not require breaks, repositioning, or subjective decision-making about which areas to examine.
Solution Approach 2:
The system performs preliminary actions by pre-programming the inspection route and automatically capturing images of all designated areas before analysis. The machine learning model is pre-trained on extensive damage datasets, enabling it to quickly and accurately identify various damage types without requiring time-consuming manual assessment during the inspection. This preliminary preparation ensures comprehensive coverage while minimizing on-site inspection duration.
3Measurement precision
If automated image analysis is implemented, then damage detection accuracy improves, but device complexity increases due to machine learning requirements
Solution Approach 1:
The patent introduces a server as an intermediary component that hosts the machine learning models and processes images remotely. Rather than embedding complex AI algorithms in the mobile device, the system uses the server as a mediator to perform the computationally intensive image analysis. This architecture maintains high damage detection accuracy through sophisticated machine learning while reducing device complexity on the client side, as the mobile device only needs basic image capture and communication capabilities.
4Reliability
If navigation guidance is provided to users, then inspection thoroughness improves, but ease of operation deteriorates due to additional instructions and monitoring requirements
Solution Approach 1:
The system replaces manual navigation and inspection guidance with automated robotic or guided movement mechanisms. The inspection device autonomously navigates through the property following pre-planned routes, eliminating the need for human operators to manually move to each inspection point. This substitution maintains thorough inspection coverage while improving ease of operation, as the automated navigation system handles the complexity of movement and positioning without requiring user intervention or interpretation of navigation instructions.
Data Source
AI summary
A system and method for a guided inspection of an apartment, home or other physical space is disclosed. The system and method use augmented reality to guide a user through a physical space. The system and method further use machine learning to automatically detect and classify damage to various physical structures in the physical space. In response to detected damage, the system may prompt a user to move closer to the detected damage for further inspection. The system can also detect obscured structures and prompt a user to make changes to the environment to increase the visibility of the obscured structures.