Appearance Inspection Image Generation for Defect Training Diversity
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Solution Overview
Problem
Existing pseudo-defective image creation devices generate a limited number of pseudo-defective images, leading to insufficient accuracy in identifying defects due to the scarcity of training images of defective products.
Innovation Solution
An image generation device that generates training images by extracting features from non-defective products and combining them with non-defective images to create pseudo-defective images, enhancing the variety and accuracy of training data through hue replacement, noise addition, boundary combination, and background integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If pseudo-defective images are created from a small number of defective product images, then the number of training images is increased, but the types of training images remain limited and identification accuracy is not sufficiently improved
Solution Approach 1:
The patent creates synthetic defective images by copying and combining features from non-defective product images with defect information. Instead of merely duplicating existing defective images, the system generates new pseudo-defective images by transferring defect characteristics onto non-defective product backgrounds, thereby increasing both quantity and diversity of training data
Solution Approach 2:
The patent applies parameter changes by modifying various image characteristics including color, brightness, contrast, and defect position parameters when generating pseudo-defective images. This allows the creation of diverse training images with varying visual parameters while maintaining realistic product appearances, directly addressing the limitation of uniform pseudo-defective image types
2Quantity of substance
If the number of defective products is small, then the number of training images obtained by imaging defective products is insufficient, but increasing the number of defective products is not feasible
Solution Approach 1:
The patent uses image copying and synthesis techniques to create artificial defective product images without requiring physical defective products. By copying defect patterns and superimposing them onto images of non-defective products, the system generates sufficient training data while avoiding the need to accumulate rare defective physical samples
Solution Approach 2:
The patent introduces computer-generated defect masks and transfer images as intermediaries between non-defective product images and synthetic defective product images. These intermediary elements enable the transformation of available non-defective images into training data for defect detection without requiring actual defective products
Data Source
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AI summary
An image generation device and an appearance inspection device which can increase the number of types of training image are provided. An image generation device includes an image acquisition unit configured to acquire a first image; and an image generator configured to generate a training image based on the first image, wherein the first image depicts a first defect included in a first product of a different type the product to be inspected, and the training image is a image to be read by an identifier that identifies whether an inspection image obtained by imaging the product to be inspected depicts a same type of defect as the first defect and to be used by the identifier to perform learning.