Surface Anomaly Detection Using Clustered Reflection Luminance
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Solution Overview
Problem
Existing surface anomaly detection methods struggle to accurately identify anomalous portions on complex structures, often leading to false detections or missed anomalies, particularly in complex structures where visual inspection is subjective and labor-intensive.
Innovation Solution
A surface anomaly detecting device that divides a structure into clusters based on position information, couples relevant clusters to form groups, determines a reflection luminance normal value for each group, and identifies anomalies by comparing this value to individual point luminance values, thereby enhancing detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection is used to identify anomalous portions on complex structures, then the inspection process is simple and requires minimal equipment, but the detection accuracy is low and subjective determinations are made
Solution Approach 1:
The patent segments the complex structure into multiple clusters based on spatial proximity and surface characteristics. Each cluster is then processed independently to determine local reflection luminance normal values, which improves detection accuracy by accounting for local surface variations while reducing the overall system complexity through modular processing
Solution Approach 2:
The patent replaces manual visual inspection with an automated optical measurement system using laser light sources and photodetectors to measure reflection luminance. This substitution eliminates subjectivity and improves measurement precision while the computational algorithms automatically handle the complexity of analysis
2Extent of automation
If laser range-finding device is used to acquire three-dimensional structure and luminance information, then automatic detection is achieved, but false detections occur on complex structures
Solution Approach 1:
The patent calculates separate reflection luminance normal values for each cluster based on the specific surface characteristics and lighting conditions of that local region. This local quality approach allows the system to adapt to varying surface properties and lighting conditions, reducing false detections while maintaining automatic detection capability
Solution Approach 2:
The system uses the measured reflection luminance values as feedback to dynamically determine normal values for each cluster and compare them against threshold criteria. This feedback mechanism allows the system to automatically adjust its detection criteria based on actual measurements, improving reliability while maintaining automation
3Area of stationary object
If inspection is performed on complex structures with varying surface properties, then comprehensive coverage is achieved, but the workload increases and subjective determination is made
Solution Approach 1:
The patent divides the entire surface into multiple clusters that can be processed in parallel, enabling comprehensive coverage of complex structures. This segmentation allows the system to efficiently process large surfaces by treating manageable portions independently, reducing inspection time while maintaining thoroughness
Solution Approach 2:
The system automatically determines reflection luminance normal values for each cluster based on the measured data and comparison criteria, eliminating the need for manual analysis. This self-service capability allows comprehensive surface inspection to be performed automatically, reducing both time and subjectivity
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 allows for more accurate identification of anomalous portions on complex structures, reducing false positives and enabling prompt repair by analyzing reflection luminance values across clustered points.
Implementation Method 1
a laser range-finding device can acquire a three-dimensional structure of an object (a structure) and is often equipped with a function of measuring the luminance of the received laser light as well as the position information of points on the surface of the three-dimensional object. Typically, the luminance of received light, that is, the reflection luminance from an object is dependent on the condition of the surface of the object irradiated by a laser.
Data Source
AI summary
The present disclosure is directed to providing a surface anomaly detecting device, a system, a method, and a non-transitory computer-readable medium that can identify an anomalous portion on a surface of a complex structure. A surface anomaly detecting device according to the present disclosure includes: dividing means configured to divide a structure into a plurality of clusters based on position information of a plurality of points on a surface of the structure; coupling means configured to create a cluster group by coupling together two or more of the clusters; determining means configured to determine a reflection luminance normal value of the cluster group based on a distribution of reflection luminance values at a plurality of points on a surface of the cluster group; and identifying means configured to identify an anomalous portion on the surface of the cluster group.