Automated AMOLED Panel Detection Using Template Image Matching
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Solution Overview
Problem
Conventional methods for detecting and classifying active matrix organic light emitting diode (AMOLED) panels rely heavily on manual observation, leading to subjective results, reduced accuracy, and potential vision damage due to prolonged exposure, especially in large-scale production.
Innovation Solution
An automated method and apparatus that compare images from AMOLED panels with a preset template image library to classify them, using image processing techniques to reduce human error and improve efficiency, involving the use of Charge-Coupled Device (CCD) image collection and similarity matching with fine and fault template images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation method is used for detecting and classifying AMOLED panels, then operators can directly observe the panels after lighting, but great manpower is necessary and detection results are affected subjectively by operators
Solution Approach 1:
The patent replaces the manual mechanical observation system with an automated image processing system. Specifically, it uses image collection devices to capture AMOLED panel images, then applies image processing algorithms to automatically detect and classify defects, replacing human operators entirely and eliminating subjective detection errors.
Solution Approach 2:
The patent introduces an intermediary image processing system between the AMOLED panel and the final classification result. This intermediary system includes image collection, preprocessing, defect detection, and classification modules that objectively analyze panel quality without direct human involvement in the detection process.
2Productivity
If manual observation method is used for detecting and classifying AMOLED panels, then operators can visually inspect the panels, but excessive use of eyes may cause damage to vision and speed of detection is reduced
Solution Approach 1:
The patent replaces the human visual system with an automated image collection and processing system. Image collection devices capture panel images, and computer-based algorithms perform defect detection and classification, completely eliminating the need for operators to visually inspect panels and thus preventing vision damage while increasing detection speed.
Solution Approach 2:
The patent enables the AMOLED panel detection system to perform self-inspection through automated image processing. The system collects images, processes them through algorithms, and automatically classifies defects without requiring human operators, making the detection process self-sufficient and highly efficient.
3Productivity
If manual observation method is used for detecting and classifying AMOLED panels, then operators can inspect the panels, but in large scale production great manpower will be necessary
Solution Approach 1:
The patent replaces the manual labor-based detection system with an automated image processing system. The system uses image collection devices to capture panels and algorithms to detect and classify defects automatically, eliminating the need for large numbers of operators and significantly improving production efficiency in large-scale manufacturing.
Solution Approach 2:
The patent creates a universal automated detection system that can handle multiple AMOLED panels simultaneously through batch image processing. The image processing algorithms can analyze numerous panels in parallel, making the system adaptable to large-scale production requirements without proportionally increasing manpower needs.
Data Source
AI summary
A method and an apparatus are provided for detecting and classifying an active matrix organic light emitting diode panel. The method includes: comparing images to be compared which are collected from the active matrix organic light emitting diode panel with template images in a preset template image library; classifying the active matrix organic light emitting diode panel depending on results of the comparing.