Dynamic Threshold Inspection for CAD Line Segment Deviation
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Solution Overview
Problem
Existing methods struggle to accurately identify manufacturing defects in products by superimposing CAD data onto images, as the deviation between projected 3D line segments and feature lines is difficult to quantify due to varying camera positions and orientations, making it challenging to set appropriate threshold values for defect determination.
Innovation Solution
An inspection apparatus that detects feature lines from images, correlates them with 3D line segments, generates projection lines, sets a threshold value based on statistical error, and classifies combinations to accurately identify defective portions by dynamically adjusting the threshold value based on error statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed threshold value is used to determine manufacturing defects, then the inspection process is simple, but the accuracy of defect identification deteriorates due to varying camera positions and orientations
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed threshold value to a dynamic threshold determination method. The threshold is no longer static but adapts based on the actual measured deviations between projected 3D line segments and feature lines detected in images. This allows the inspection system to adjust its criteria automatically according to varying camera positions and orientations, thereby maintaining high accuracy without complicating the inspection process.
Solution Approach 2:
The patent changes the parameter of the threshold value from a fixed constant to a variable determined through statistical analysis of measurement deviations. By calculating the distribution of deviations between projected lines and detected feature lines, the system dynamically sets appropriate threshold values that adapt to different inspection conditions, resolving the contradiction between operational simplicity and measurement precision.
2Reliability
If the threshold value is set to be sensitive to detect all defects, then defect detection capability is improved, but false positives increase
Solution Approach 1:
The patent implements feedback by using the actual measured deviations from multiple correspondences between projected 3D line segments and feature lines to determine the threshold value. The system collects statistical information about the distribution of deviations and uses this feedback to set an appropriate threshold that balances defect detection sensitivity with false positive reduction. This feedback mechanism ensures that the threshold is neither too loose nor too strict.
Solution Approach 2:
The patent applies partial action by not requiring absolute precision for every single correspondence, but rather using the collective statistical information from multiple correspondences to determine the threshold. By analyzing the distribution of deviations across multiple line segment-feature line pairs, the system identifies an appropriate threshold level that achieves reliable defect detection without excessive sensitivity that would cause false positives.
3Measurement precision
If multiple correspondences between 3D line segments and feature lines are used, then the accuracy of position and orientation estimation is improved, but the computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the object into multiple line segments and finding correspondences between these segments and feature lines in the image. Instead of treating the entire object as a single entity, the system breaks it down into manageable segments, which can be processed independently. This segmentation approach allows the use of multiple correspondences to improve accuracy while keeping the computational complexity manageable through modular processing.
Data Source
AI summary
An inspection apparatus includes a memory for storing shape information including a plurality of line segments representing a shape of an object; and a processor coupled to the memory and the processor that detects a plurality of feature lines from an image of the object, generates a plurality of combinations obtained by correlating each of the plurality of line segments and each of the plurality of feature lines with each other, generates a plurality of projection lines by projecting each of the plurality of line segments onto the image, sets a threshold value with respect to an error between a position of the projection lines and a position of the feature lines of the line segments included in each of the plurality of combinations based on a statistical value of the error, and classifies the plurality of combinations using the threshold value.