Semantic Segmentation Rendering UAV Structure Inspection
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Solution Overview
Problem
Current 3D scan approaches for structure inspection using UAVs are limited by high-fidelity processing requirements, generating large amounts of data and focusing only on geometric information, which does not allow for precise localization or identification of specific structure components, requiring manual and labor-intensive processes for component differentiation.
Innovation Solution
Implementing semantic segmentation rendering that encodes structural data in pixel values, enabling precise localization of structure components by associating identifiers with each pixel, and using shaders to visually distinguish components in 3D graphical representations, allowing for automated identification and storage of component locations for further inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity 3D scanning is used for structure inspection, then geometric information accuracy is improved, but data volume and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential geometric information needed for inspection from the captured images, rather than processing all high-fidelity 3D scan data. This is achieved by identifying and extracting key structural features and components, converting them into simplified 3D representations that retain inspection-relevant information while eliminating redundant data.
Solution Approach 2:
Instead of starting with high-fidelity 3D scanning and then filtering data, the patent inverts the approach by first capturing images, extracting essential geometric information, and only then generating 3D representations. This reversal allows the system to obtain sufficient geometric accuracy for inspection purposes without the overhead of processing complete high-fidelity scans.
2Measurement precision
If high-fidelity 3D scanning is used for structure inspection, then geometric information accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential geometric information needed for inspection from the captured images, rather than processing all high-fidelity 3D scan data. This is achieved by identifying and extracting key structural features and components, converting them into simplified 3D representations that retain inspection-relevant information while eliminating redundant data.
Solution Approach 2:
The patent applies partial action by processing only the portions of the structure that are relevant to the inspection objectives. Instead of performing complete high-fidelity scanning and processing of entire structures, the system selectively processes specific components and features that require inspection, reducing overall processing time and computational requirements.
3Adaptability or versatility
If manual processes are used for component differentiation, then flexibility and adaptability are maintained, but labor intensity and time consumption increase
Solution Approach 1:
The patent implements self-service by enabling the inspection system to automatically identify, differentiate, and process structural components without manual intervention. The system uses image processing and pattern recognition to autonomously distinguish between different structure components, automatically generate 3D representations, and prepare inspection data, thereby eliminating the need for manual component differentiation while maintaining adaptability to various structure types.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational methods. Instead of human operators manually differentiating and recording component information, the system uses computer vision algorithms, image processing techniques, and automated 3D reconstruction methods to perform these tasks, significantly improving productivity while maintaining the flexibility to handle diverse structure types.
4Reliability
If comprehensive data capture is performed for all structure components, then inspection completeness is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The patent extracts only the essential geometric information needed for inspection from the captured images, rather than processing all high-fidelity 3D scan data. This is achieved by identifying and extracting key structural features and components, converting them into simplified 3D representations that retain inspection-relevant information while eliminating redundant data.
Solution Approach 2:
The patent segments the structure into distinct components and processes each component separately, capturing only the data necessary for inspecting each specific element. This segmentation approach allows the system to maintain inspection completeness by ensuring all necessary components are covered, while reducing overall data storage requirements by avoiding redundant capture of entire structures at maximum fidelity.
Data Source
AI summary
Semantic segmentation rendering is performed to encode structural data, including precise and relative component locations, in pixel values of an image depicting a structure. Images captured during a UAV-based exploration inspection of a structure are obtained. In each pixel value that corresponds to the structure within the images, identifiers of the structure, a component of the structure depicted using the pixel value, and a location of the component are encoded. The pixel values of the images are segmented into polygons according to the encoded identifiers, and data indicative of the polygons is stored for use in a further inspection of the structure. In connection with the semantic segmentation rendering, a three-dimensional graphical representation of the structure is obtained and rendered, according to the encoded identifiers, using shaders that visually distinguish each component of the structure, in which the data indicative of the polygons identifies respective ones of the shaders.