Inspection Apparatus for Printed Material Image and Data Defect Detection
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Solution Overview
Problem
Existing inspection systems can address image defects in printed materials but fail to handle data defects, such as missing pages or inconsistencies between the obverse and reverse sides, which are sources of printed material issues.
Innovation Solution
An inspection apparatus that reads printed materials, detects image defects by comparing inspection images with reference images, and identifies data defects by collating specific data from the inspection images with pre-set correct data, enabling comprehensive defect detection and recovery possibilities determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only image defect detection is performed by comparing inspection images with reference images, then image defects can be detected, but data defects such as missing pages or inconsistencies between obverse and reverse sides cannot be detected
Solution Approach 1:
The inspection system is divided into two independent detection modules: one for image defects (comparing inspection images with reference images) and another for data defects (collating specific data from inspection images with correct data). This segmentation allows each module to specialize in its respective defect type while working together to provide comprehensive inspection coverage.
Solution Approach 2:
The inspection apparatus is designed to perform multiple functions: it can detect both image defects (through image comparison) and data defects (through data collation). The system universally handles different types of defects by integrating multiple detection mechanisms into a single inspection platform.
2Reliability
If comprehensive defect detection is implemented by adding data defect detection, then both image and data defects can be detected, but the complexity of the inspection system increases
Solution Approach 1:
The image defect detection module and data defect detection module are merged into a single integrated inspection apparatus. Both modules share common infrastructure such as the inspection image acquisition system and processing platform, reducing overall system complexity while maintaining comprehensive detection capability.
Solution Approach 2:
The inspection apparatus is designed to perform multiple functions: it can detect both image defects (through image comparison) and data defects (through data collation). The system universally handles different types of defects by integrating multiple detection mechanisms into a single inspection platform.
3Reliability
If dual collation processes are performed for comprehensive inspection, then both image and data defects can be detected, but the inspection time increases
Solution Approach 1:
The image defect detection and data defect detection processes are executed in parallel rather than sequentially. The inspection apparatus continuously performs both collation operations simultaneously on the inspection images, maintaining continuous useful action and avoiding time loss from sequential processing.
Solution Approach 2:
The correct data used for data collation is prepared and stored in advance before the inspection process begins. This preliminary preparation of reference data eliminates the need for time-consuming data setup during actual inspection, reducing overall inspection time while maintaining detection accuracy.
Data Source
AI summary
In an inspection system of a printed material, it is made possible to deal with both an image defect and a data defect. An inspection apparatus performing inspection of a printed material output from a printing apparatus detects an image defect by collating an inspection image, which is to be inspected, obtained by reading the printed material with a reference image taken as a reference of the inspection, which corresponds to the printed material. Furthermore, the inspection apparatus detects a data defect by collating specific data extracted from the inspection image with correct data corresponding to the specific data set in advance.