Screen Defect Detection Using Core-Region Gray Analysis
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Solution Overview
Problem
Existing screen defect detection methods, particularly for transparent defects caused by foreign matters on screens, are inefficient and inaccurate, leading to high rates of missed inspection and false positives.
Innovation Solution
An automated screen defect detection method and apparatus that identifies suspected defective pixel points, divides the defect region into a general and core region, and judges defects based on mean and minimum gray values, improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual detection is used for screen defects, then the detection process is simple to implement, but the detection efficiency is low and labor costs are high
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated optical detection system using imaging devices and image processing algorithms. The imaging device captures screen images, and computer vision algorithms automatically analyze them for defects, eliminating manual labor while maintaining detection capability.
Solution Approach 2:
The detection system performs self-analysis through automated image processing algorithms that independently identify and classify defects without human intervention. The system processes images, identifies suspicious regions, and determines defect types autonomously, enabling the system to serve itself in the detection process.
2Measurement precision
If manual detection is used for transparent defects, then the detection method is simple, but the detection accuracy is low with high rates of missed inspection and false positives
Solution Approach 1:
The patent segments the defect detection process into distinct stages: image acquisition, suspicious region identification, and defect type determination. Each stage uses specialized algorithms tailored to specific detection tasks, improving overall accuracy by breaking down the complex detection problem into manageable segments.
Solution Approach 2:
The patent applies different processing strategies to different regions of the screen image. Suspicious regions are identified and analyzed with specialized algorithms different from normal regions, allowing the system to focus computational resources on areas most likely to contain defects while maintaining high accuracy.
3Productivity
If automated detection is implemented, then labor costs are reduced, but the detection system becomes more complex
Solution Approach 1:
The patent designs a universal detection system that can identify multiple types of screen defects (transparent defects, scratches, dead pixels, etc.) using a single integrated platform. The imaging device and processing algorithms serve multiple detection functions simultaneously, reducing the need for separate specialized systems for each defect type.
Data Source
AI summary
A screen defect detection method and apparatus and an electronic device are disclosed. The method comprises the following steps: identifying a number of suspected defective pixel points(S110) from a detection image of a target screen; determining a suspected defect region(S120) corresponding to a suspected defective pixel point in the detection image; dividing the suspected defect region into a general region and a core region(S130); and judging whether the target screen has a transparent defect(S140) according to a mean gray value of the suspected defect region, a mean gray value of the general region and a minimum gray value of the core region.