Screen Module Defect Detection Using Cross-State Image Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the assembly or manufacturing process of screen modules of electronic devices, display defect detection is hindered by false positives due to dust, glues, or other particles adhering to the screen, leading to inaccurate identification of display defects.
Innovation Solution
A method involving obtaining two screen images of a screen module in different test states, determining image feature correlation between them, and using this correlation to verify the presence of display defects, thereby reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single image defect detection is used, then detection speed is fast, but false positive rate is high due to dust and particles
Solution Approach 1:
The patent applies preliminary action by capturing a first image before the actual defect detection. This preliminary image allows the system to identify and remove dust and particle interference in advance, ensuring that subsequent defect detection is based on clean image data, thereby提高 accuracy without adding complex real-time processing during detection
Solution Approach 2:
The patent segments the detection process into distinct phases: capturing the first image, identifying defect areas, capturing the second image, and then performing defect detection. This segmentation allows the system to handle different tasks at different stages, separating the concerns of image acquisition, interference removal, and actual defect analysis, which improves overall accuracy while maintaining manageable system complexity
2Measurement precision
If multiple images are captured and processed, then defect detection accuracy is improved, but detection time increases
Solution Approach 1:
The first image is captured as a preliminary step before defect detection begins. By preparing and processing this preliminary image in advance to remove dust and particles, the system avoids the need for repeated captures or complex real-time processing during the actual detection phase, thus reducing overall detection time while maintaining high accuracy
Solution Approach 2:
The patent extracts and removes dust and particle interference from the image processing pipeline by using the first image to identify and eliminate these unwanted elements. This extraction approach separates the defect detection task from the interference removal task, allowing the second image to be processed more efficiently for defect detection alone, thereby reducing detection time
Data Source
AI summary
A defect detection method includes obtaining a first screen image and a second screen image of a screen module, in response to determining a defect area in the first screen image, determining an image feature correlation between the first screen image and the second screen image, and determining a defect detection result of the screen module based on the image feature correlation. The first screen image is an image displayed by a display screen collected when the display screen of the screen module is in a first test state. The second screen image is an image displayed by the display screen collected when the display screen is in a second test state. The defect area indicates that the screen module has a display defect.