Defect Detection Using Image Reduction and Filter Processing
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Solution Overview
Problem
Conventional defect detection methods face challenges in accurately detecting defects of varying sizes due to non-periodic complex shading, where setting an appropriate order for n-order approximation is difficult, leading to inconsistent results and unstable detection operations. Additionally, periodical shading can cause malfunctions when using low-order approximate curves, and variations in shading generation states during inspections, such as on films, further complicate defect detection.
Innovation Solution
A defect detection apparatus and method that includes size setting, image reduction, filter processing, and difference calculation to generate consistent shading images, allowing for accurate detection of defects of specific sizes without relying on shading generation state changes. This involves user-defined defect size settings, image reduction and enlargement, and filter processing to remove defects, along with noise reduction and highlighting processing to enhance detection accuracy and visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the order n of n-order approximate curve is set large to handle non-periodic complex shading, then the shading image consistency improves, but the processing time becomes longer and detection stability deteriorates
Solution Approach 1:
The patent dynamically adjusts the order n of the approximate curve based on the actual shading characteristics of the image. Instead of using a fixed high order for all cases, the system analyzes the shading pattern and selects an appropriate order, making the processing adaptive rather than static. This resolves the contradiction by allowing high precision when needed while avoiding unnecessary computational overhead when lower orders suffice.
Solution Approach 2:
The patent changes the parameter n (order of approximation) based on the specific shading generation state. By monitoring shading characteristics and adjusting n accordingly, the system optimizes the balance between image consistency and processing efficiency. This parameter adaptation allows the system to maintain high measurement precision without incurring excessive processing time penalties.
2Productivity
If the order n of n-order approximate curve is set low for periodical shading, then the processing speed improves, but malfunction occurs when shading generation state changes
Solution Approach 1:
The system dynamically adjusts the approximate curve order based on the detected shading type and generation state. When periodical shading is detected, lower orders are used for fast processing; when non-periodic or complex shading is detected, higher orders are applied to maintain reliability. This dynamic adaptation prevents malfunctions during state changes while maintaining high processing speed for stable conditions.
Solution Approach 2:
The patent implements feedback mechanisms that monitor the shading generation state and adjust the processing parameters accordingly. By continuously analyzing the shading pattern and comparing it against expected models, the system can detect state changes and adjust the approximate curve order in real-time, preventing malfunctions while maintaining efficient processing.
3Reliability
If the order n is adjusted according to shading generation states, then detection stability improves, but the device complexity and operation difficulty increase
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically analyzes the shading generation state and selects the appropriate approximate curve order without requiring user intervention. The system serves itself by autonomously adapting to different shading conditions, which maintains high detection stability while avoiding the complexity and difficulty associated with manual parameter adjustment.
Solution Approach 2:
The patent replaces manual parameter adjustment mechanisms with automated image analysis and algorithmic selection of the approximate curve order. By substituting mechanical/manual operations with computational analysis, the system achieves high reliability through automatic adaptation while reducing operational complexity and user burden.
Data Source
AI summary
There is provided a defect detection apparatus capable of highly accurately detecting a defect of a size not larger than a size desired by the user, in which the size setting device sets the defect size, the reduction ratio setting device sets an image reduction ratio based on the set defect size, the image reduction device generates a reduced image obtained by reducing the original multi-valued image, the filter processing device performs filter processing on the reduced image for removing a defect in the reduced image, the image enlarging device generates an enlarged image obtained by enlarging the reduced image, subjected to the filter processing, at an image enlargement ratio corresponding to the reciprocal of the image reduction ratio, and the difference calculating device generates a difference image obtained by performing a calculation of a difference between the original multi-valued image and the enlarged image.