Defect Detection Method Using Color Difference Thresholds
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Solution Overview
Problem
Current defect detection methods suffer from low accuracy due to reasonable errors such as slight chromatic aberration and background noise in image processing, which affect the reliability of defect identification in products.
Innovation Solution
The method involves an electronic device that calculates a color difference threshold and a first threshold based on positive and negative sample images to distinguish target and background pixel points, and determines feature connected regions in test images, thereby improving detection accuracy by separating target regions from background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional defect detection methods are used, then the detection process is simple, but the detection accuracy is low due to reasonable errors such as chromatic aberration and background noise
Solution Approach 1:
The patent segments the image processing into distinct modules: acquiring sample images, calculating color difference thresholds, determining pixel point differences, generating feature connected regions, and identifying target regions. This segmentation allows each module to handle specific aspects of the detection task, improving overall accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
The patent performs preliminary actions by acquiring positive and negative sample images before processing test images. These samples are used to calculate color difference thresholds and establish baseline characteristics, which then guide the defect detection process. This preliminary preparation enables more accurate distinction between normal variations and actual defects in subsequent testing
2Reliability
If threshold values are set to identify defects, then defect detection capability is improved, but false detection increases due to reasonable errors in images
Solution Approach 1:
The patent applies local quality by calculating color difference thresholds specifically for different regions and types of pixel points. Instead of using a single universal threshold, the system determines thresholds based on local characteristics of positive and negative sample images, allowing for more precise distinction between target regions and background noise in different contexts
Solution Approach 2:
The patent introduces an intermediary approach by using calculated color difference thresholds as a mediator between the raw image data and the final defect identification. These thresholds act as a filtering mechanism that separates reasonable errors from actual defects, improving reliability by preventing false detections while maintaining precision through calibrated threshold values
Data Source
AI summary
A defect detection method applied to an electronic device includes determining, pixel difference values based a test sample image and positive sample images. A color difference threshold is determined according to positive sample images. Feature connected regions of the test sample image are generated according to the color difference threshold and pixel difference values. A first threshold is generated according to image noises of positive sample images. A target region is determined from the feature connected regions according to a number of pixel points in each feature connected region and the first threshold. Once a second threshold is determined according to defective pixel points of negative sample images, a detection result of a test sample is determined according to an area of the target region and the second threshold.