Paper Thickness Detection Using Gray Value Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Digital Information System (DIS) sensors inaccurately distinguish paper thickness from crease features, leading to low accuracy in thickness detection due to misjudging creases as thickness changes.
Innovation Solution
A method and apparatus that extract first and second thickness features from a paper image, with average processing of gray values to differentiate between creases and actual thickness changes, using thresholds to determine if a region has changed in thickness or is affected by creases or noise, and restoring gray values to correct for interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the DIS sensor is used to detect paper thickness, then the detection process is simple and fast, but the detection accuracy is low due to misjudging crease features as thickness changes
Solution Approach 1:
The detection process is segmented into multiple stages: initial thickness image acquisition, feature extraction and classification (dividing features into first thickness features with ≤threshold pixels and second thickness features with >threshold pixels), selective average processing, and final determination. This segmentation allows the system to process most pixels efficiently while applying enhanced processing only where needed, maintaining speed while improving accuracy.
Solution Approach 2:
Different processing strategies are applied to different regions of the paper based on their characteristics. Regions with first thickness features (small pixel count, likely creases) undergo average processing to smooth out noise, while regions with second thickness features (large pixel count, likely actual thickness changes) are analyzed more precisely. This local differentiation optimizes both speed and accuracy for each region type.
2Loss of information
If the DIS sensor detects all thickness features, then no information is lost, but crease features are misjudged as thickness changes
Solution Approach 1:
Before final thickness determination, the system performs preliminary classification of thickness features by pixel count and applies average processing to identify and filter out crease features. This preliminary action removes false positives before the final thickness measurement, ensuring that only genuine thickness changes are detected while preserving all actual thickness information.
Solution Approach 2:
The system uses feedback from the pixel count analysis and average processing results to adjust the detection process. By comparing the number of pixels in each thickness feature against the threshold and analyzing gray value changes, the system feedbacks into the determination process to distinguish between creases and actual thickness changes, improving overall detection accuracy.
Data Source
AI summary
Provided are a method and an apparatus for detecting a thickness of paper, and a storage medium. The method includes: acquiring a first thickness image of a target paper; extracting at least one first thickness feature and at least one second thickness feature from the first thickness image in the first thickness image; performing average processing on gray values of multiple pixel points in the at least one first thickness feature to acquire a first gray value, and the average processing is performed on gray values of the multiple pixel points in the at least one second thickness feature to acquire a second gray value; when the first gray value is smaller than the second gray value of multiple pixel points in at least one second thickness feature, determining that a thickness of a region, corresponding to the at least one second thickness feature, on the target paper has changed.