UAV Semantic Structure Inspection for Targeted Component Analysis
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Solution Overview
Problem
Existing 3D scan approaches for structure inspection using UAVs are not suitable for all situations, as they require high-fidelity processing, capture large amounts of data, and provide only geometric reconstructions, lacking semantic understanding and focusing on specific components.
Innovation Solution
Implementing UAV-based semantic understanding systems that use computer vision and machine learning to semantically determine components of a structure from captured images, generating visual representations such as 3D graphical or hierarchical text representations, and allowing users to select components for further inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity processing is used for 3D scan approaches, then measurement precision is improved, but data capture requirements increase and device complexity increases
Solution Approach 1:
The patent extracts and processes only the essential geometric and semantic features from captured images rather than processing all raw data. The system identifies and processes key structural components and their relationships, discarding redundant data while maintaining measurement precision for critical elements.
Solution Approach 2:
The inspection process is segmented into distinct phases: initial exploration inspection for component identification, followed by targeted detailed inspection of selected components. This segmentation allows the system to capture comprehensive data initially, then focus processing resources only on relevant areas, reducing overall data capture requirements while maintaining precision where needed.
2Measurement precision
If comprehensive 3D scanning is performed, then measurement precision is improved, but inspection time increases
Solution Approach 1:
The system performs a preliminary exploration inspection that captures comprehensive geometric data and identifies all structural components with their semantic meanings. This preliminary action establishes a complete digital model and component inventory, enabling subsequent targeted inspections to focus only on specific components of interest, thereby reducing total inspection time while maintaining measurement precision.
Solution Approach 2:
The patent implements a two-stage approach where the first stage performs comprehensive scanning beyond what any single inspection task requires, creating a reusable digital model. Subsequent inspections can then perform partial, targeted scans of specific components, avoiding redundant comprehensive scans and significantly reducing inspection time while maintaining precision.
3Device complexity
If geometric reconstruction only is provided, then device complexity is reduced, but information completeness deteriorates
Solution Approach 1:
The patent introduces semantic interpretation as an intermediary layer between geometric reconstruction and inspection analysis. The system first creates geometric reconstructions of structural components, then applies computer vision and machine learning models to interpret these geometries semantically, identifying components such as beams, columns, and joints. This intermediary semantic layer enriches the information content without significantly increasing device complexity, as the same hardware performs both geometric and semantic analysis.
Data Source
AI summary
An unmanned aerial vehicle (UAV) performs operations to semantically understand components of a structure under inspection. During an exploration inspection of the structure, a camera of the UAV captures images of the structure. Components of the structure are determined based on the images and a taxonomy associated with the structure, for example, using a computer vision process and a machine learning model. A visual representation of the components (e.g., a semantic scene graph, such as a three-dimensional graphical representation of a hierarchical text representation) is generated and output to a user device in communication with the UAV to enable selections, via a graphical user interface output for display at the user device, of ones of the components for further inspection using the UAV.