GAN Defective Image Generation for Product Inspection
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Solution Overview
Problem
Current methods for generating defective image samples for product defect detection face challenges in accuracy due to the scarcity of diverse defect samples, leading to suboptimal performance in classification networks.
Innovation Solution
A method utilizing a Generative Adversarial Network (GAN) with an autoencoder to generate defective images by combining flawless sample images with noise vectors, calculating loss values, and optimizing the GAN to produce comprehensive and accurate defective image datasets for training defect detection models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional defect detection methods are used with limited sample data, then the detection process is simple, but the detection accuracy is low due to lack of diverse defect samples
Solution Approach 1:
The patent uses a generative adversarial network (GAN) to create synthetic copies of defect samples. The generator creates realistic defective images by learning from limited real defect samples and flawless product images, effectively multiplying the available training data without requiring physical collection of diverse defective products.
Solution Approach 2:
The patent transforms the parameter space by using GANs to generate defect samples with varying characteristics. The system learns to produce diverse defect types, positions, and patterns by adjusting the generative model's parameters during training, enabling creation of comprehensive defect datasets from limited initial samples.
2Measurement precision
If more diverse defect samples are collected to improve detection accuracy, then the accuracy improves, but the sample collection process becomes more difficult and time-consuming
Solution Approach 1:
The patent performs preliminary action by pre-training the GAN model with available defect samples before actual defect detection is needed. This advance preparation creates a comprehensive synthetic defect dataset that can be immediately used for training detection models, eliminating the need for time-consuming sample collection during deployment.
Solution Approach 2:
Instead of physically collecting diverse defective samples which is time-consuming, the system creates synthetic copies through the GAN model. This digital replication process generates diverse defect variations instantly once the model is trained, saving significant time compared to manual sample collection.
3Measurement precision
If more diverse defect samples are collected to improve detection accuracy, then the accuracy improves, but the complexity of the data collection and processing system increases
Solution Approach 1:
The patent replaces complex physical sample collection and processing systems with a computational copying approach. The GAN model generates synthetic defect samples algorithmically, eliminating the need for complex hardware systems for collecting, storing, and managing physical defective samples while achieving the same goal of diverse training data.
Solution Approach 2:
The patent substitutes mechanical/sample-based defect collection systems with a computational model. Instead of physically gathering diverse defective products through complex logistics and processing, the system uses software-based GAN generation to create synthetic defect samples, replacing physical complexity with computational simplicity.
Data Source
AI summary
A method for generating defective image of products applied in an electronic device includes generating first input data according to flawless sample images and a first noise vector, using an autoencoder as a generator of a Generative Adversarial Network (GAN), inputting the first input data to the generator, and generating images for training in defects. The method further includes calculating a first loss value between the flawless sample images and the defect training images, inputting the defect training images into a discriminator of the GAN, and calculating a second loss value. The method further includes obtaining an optimized GAN and taking the optimized GAN as a defective image adversarial network, obtaining flawless testing images, inputting the flawless testing images and a second noise into a generator of the defective image adversarial network, and generating images of defects by processing the flawless testing images and the second noise.