Waveguide Image Anomaly Detection for Automated Defect Screening
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Solution Overview
Problem
Human error and bottlenecks in waveguide evaluation processes during manufacturing due to the reliance on human intervention for defect detection in waveguides, leading to inefficiencies and potential defects in radio-frequency (RF) waveguides.
Innovation Solution
A computer-implemented method and system that uses image data from waveguides to compute statistical values, generate an extremity matrix based on an anomaly detector model, and determine whether a waveguide is conforming or non-conforming using pre-computed probability distributions and thresholds, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention is used for waveguide defect detection, then measurement precision can be maintained through expert judgment, but productivity decreases due to manual evaluation bottlenecks
Solution Approach 1:
The patent replaces the mechanical human evaluation process with an automated computational system that uses image processing and statistical analysis. The system captures images of waveguides, computes statistical values of pixels at various image locations, generates extremity matrices, and automatically determines conformance without human intervention, thereby maintaining detection precision while dramatically improving productivity.
Solution Approach 2:
The system enables waveguide defect detection to be self-performing through automated image analysis. The computational method independently processes waveguide images, computes statistical metrics, generates extremity matrices, and makes conformance determinations without requiring external human evaluation, thus eliminating bottlenecks while preserving accuracy.
2Reliability
If human evaluation is used for waveguide inspection, then complex defect patterns can be identified through experience, but human error increases and consistency decreases
Solution Approach 1:
The patent transforms the qualitative human evaluation process into quantitative parameter-based analysis. By computing statistical values of pixels, generating extremity matrices with specific threshold comparisons, and using standardized computational algorithms, the system achieves consistent, repeatable results that eliminate human error and variability while maintaining reliable defect detection.
3Productivity
If automated image processing is implemented, then productivity increases and human error decreases, but measurement precision may deteriorate without expert judgment
Solution Approach 1:
The patent segments the waveguide image into multiple image locations and computes statistical values for pixels at each location independently. This segmentation allows the automated system to analyze complex defect patterns through systematic breakdown of the image into manageable regions, maintaining detection precision while enabling high-speed automated processing.
Solution Approach 2:
The patent introduces additional analytical dimensions by computing statistical values across multiple image locations and generating extremity matrices that add layers of computational analysis. This multi-dimensional approach allows the automated system to capture complex defect characteristics that would require expert human judgment, thereby maintaining precision while achieving high productivity.
Data Source
AI summary
Systems and methods are described herein for waveguide defect detection. In some examples, a statistical value of pixels of a number of image locations in one or more images of the waveguide can be computed. An extremity matrix can be generated based on an anomaly detector model and statistical pixel data that includes statistical values computed for respective image locations in the one or more images of the waveguide. A decision can be made to determine whether the waveguide is a conforming or a non-conforming waveguide based on the extremity matrix and a set of thresholds. In some examples, the statistical value is a mean intensity value or an average intensity value.