Screen Defect Detection Model Selection for Reliable Production Use
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Solution Overview
Problem
Deploying a trained deep learning model in a production line for screen defect detection is challenging due to equipment, parameter, and environmental interference, affecting the prediction effect and requiring manual intervention for defect classification.
Innovation Solution
A method for determining a target model involves acquiring a sample data set, dividing it into training and verification sets, training multiple models, testing them, and selecting the best model based on verification and offline test results, adjusting confidence thresholds, and deploying the model only when meeting predetermined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a deep learning model is deployed for screen defect detection, then manual workload is reduced and efficiency increases, but the prediction effect is affected by equipment, parameter, and environmental interference
Solution Approach 1:
The patent applies preliminary action by conducting extensive model training and verification before deployment. Multiple models are trained with different parameters, and their predictions are verified against ground truth data. The best model is selected based on verification results, ensuring optimal performance before actual production use. This preliminary preparation mitigates the reliability issues that would otherwise occur during production due to environmental interference.
Solution Approach 2:
The patent implements feedback mechanisms by comparing model predictions with ground truth data during verification. The verification results are used to evaluate model performance and guide model selection. This feedback loop ensures that only models meeting accuracy requirements are deployed, maintaining high reliability while achieving automated defect detection.
2Measurement precision
If multiple models are trained and verified to ensure accurate defect classification, then detection accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the model development process into distinct phases: training multiple models with different parameters, verifying each model against ground truth data, and selecting the best model based on verification results. This segmented approach allows systematic evaluation of different model configurations to achieve accurate defect classification while managing training time through structured parallel processing.
Solution Approach 2:
The patent utilizes parameter changes by training models with different hyperparameters and configuration settings. By varying training parameters and comparing verification results, the system identifies optimal parameter combinations that achieve high classification accuracy. This approach enables finding the best model efficiently without exhaustive searching of all possible parameter spaces.
Data Source
AI summary
The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.