Automated Mura Detection for LCD Spot Defects
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Solution Overview
Problem
Existing mura detection methods for large LCD devices are limited in detecting and classifying spot-shaped defects like black spots, white spots, and black-and-white spots due to subjective human error and inability to differentiate noise from actual defects, leading to increased deviation in detection as screen size increases.
Innovation Solution
A mura detection apparatus and method that analyzes image information from a display panel to detect and classify mura candidate areas, extracts feature and position information, removes non-mura, and specifically identifies spot-shaped mura by using a mura candidate area detecting unit, feature extracting unit, non-mura removing unit, and spot-shaped mura verifying unit to classify final mura based on kind, size, and position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual inspection by workers' eyes is used to detect mura defects, then detection can be performed with simple equipment, but detection accuracy decreases and deviation increases as screen size enlarges
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that captures display panel images and analyzes them using computer algorithms. This substitution eliminates human subjectivity and provides consistent, accurate detection regardless of screen size, directly resolving the contradiction between simple equipment and high detection accuracy.
Solution Approach 2:
The patent introduces an image processing system as an intermediary between the display panel and the inspection process. This intermediary captures images and performs automated analysis, serving as a bridge that maintains equipment simplicity while achieving high measurement precision through digital image processing techniques.
2Measurement precision
If automated mura detection is implemented to improve detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts the complex image processing and analysis functions from the physical inspection system, implementing them as separate software algorithms. This allows the hardware to remain relatively simple while achieving high measurement precision through sophisticated digital processing of captured images.
3Reliability
If all luminance non-uniformities are detected as mura to ensure comprehensive detection, then detection coverage improves, but false detection of noise and foreign materials increases
Solution Approach 1:
The patent applies different analysis criteria and thresholds to different regions and characteristics of detected anomalies. By analyzing local features such as shape, size, and luminance distribution patterns, the system can distinguish between actual mura defects and noise or foreign materials, maintaining comprehensive detection coverage while reducing false positives.
4Manufacturing precision
If detailed classification of spot-shaped mura is performed to improve fault categorization, then manufacturing precision improves, but detection and classification complexity increases
Solution Approach 1:
The patent analyzes luminance characteristics and patterns of detected mura defects to classify them into different types (e.g., white spot, black spot, black-and-white spot). By examining the optical properties and visual characteristics of the defects, the system achieves accurate fault classification without requiring overly complex classification mechanisms.
Data Source
AI summary
Disclosed is a mura detection apparatus and method of a display device. The mura detection method includes analyzing image information acquired from a test image displayed by a display panel to detect a plurality of mura candidate areas, extracting feature information and position information of the mura candidate areas, removing non-mura according to the features of the mura candidate areas, detecting white spot mura and black spot mura on the basis of the feature information of the mura candidate areas, detecting black-and-white spot mura on the basis of the position information of the mura candidate areas, and detecting the white spot mura, the black spot mura, and the black-and-white spot mura as final mura to classify a kind, size, and position of the final mura.