Thermal Image Anomaly Detection for Surface Structures
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Solution Overview
Problem
Existing methods for detecting anomalies on surfaces, such as roofs, often fail to distinguish between anomalies of interest and those that are not, leading to unnecessary repairs or replacements.
Innovation Solution
A method involving thermal image analysis, where a thermal image of a surface is taken and processed to identify anomalies by comparing pixel thermal values to a nominal surface value, removing non-interesting anomalies, and optionally comparing to visual images to confirm, thereby focusing repairs on relevant areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thermal image analysis is used to detect all anomalies on a surface, then the detection coverage is improved, but the ability to distinguish between anomalies of interest and non-interest anomalies deteriorates
Solution Approach 1:
The patent segments the anomaly detection process into distinct classification categories. It divides anomalies into multiple types including moisture anomalies, thermal bridge anomalies, missing insulation anomalies, and non-anomaly features. This segmentation allows the system to not only detect the presence of anomalies but also to classify and distinguish between different types of anomalies, thereby resolving the contradiction between detection coverage and anomaly distinction capability.
2Reliability
If all detected anomalies are treated as requiring repair, then no anomalies are missed, but unnecessary repairs and resource expenditure increase
Solution Approach 1:
The patent extracts and removes non-anomaly features from the set of detected anomalies through classification. By identifying and separating out features that are not actual anomalies (such as normal roof penetrations, vents, or other legitimate structural elements), the system prevents these from being incorrectly flagged as repair candidates. This extraction process maintains complete anomaly detection while eliminating false positives that would lead to unnecessary repairs and resource waste.
3Measurement precision
If comprehensive thermal and visible image comparison is performed, then all anomaly types are detected, but the complexity of the detection system increases
Solution Approach 1:
The patent uses visible light images as a reference copy or mask against which thermal images are compared. By creating a digital representation of the roof's visible features and overlaying or comparing it with the thermal image data, the system can identify thermal anomalies without requiring complex standalone thermal analysis algorithms. This copying approach simplifies the processing system while maintaining comprehensive anomaly detection capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively identifies and isolates anomalies of interest, such as moisture or energy loss issues, allowing for targeted repairs and reducing unnecessary resource expenditure.
Implementation Method 1
taking a thermal image of the surface of the structure
Data Source
AI summary
Described herein is a method of detecting anomalies on a surface of a structure. The method may comprise taking a thermal image of the surface of the structure. The method may further comprise taking a visual image of the surface of the structure. The method may then comprise conducting a thermal image numerical analysis on the thermal image. The thermal image numerical analysis may comprise obtaining a thermal image numerical value table. The thermal image numerical analysis may then comprise obtaining a surface nominal thermal value of the surface material. The thermal image numerical analysis may then comprise eliminating a first subset of pixels having a thermal value within a nominal thermal variation from the plurality of pixels. The thermal image numerical analysis may then comprise comparing the thermal value of each pixel of the plurality of pixels not in the first subset of pixels to the surface nominal thermal value to identify at least one anomaly. The thermal image numerical analysis method may then comprise removing a first number (n1) of first anomalies from the thermal image numerical analysis. Finally, the method may comprise comparing the first anomalies from the thermal image numerical analysis to the visual image.