Autoencoder-Based Appearance Defect Detection for Image Detail Preservation
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Solution Overview
Problem
Existing methods for detecting appearance defects in products suffer from reduced accuracy due to image zooming, which alters pixel numbers and leads to loss of image details.
Innovation Solution
The method involves obtaining positive and negative sample images, dividing product sample images into input blocks, and processing these blocks through a pre-trained autoencoder to reconstruct images without zooming, thereby maintaining image detail and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image zooming is applied to detect appearance defects, then the detection coverage is improved, but the image detail quality deteriorates due to pixel number changes
Solution Approach 1:
The patent divides the product image into multiple image blocks and processes each block separately through the autoencoder. This segmentation allows the system to maintain original image resolution while covering the entire product surface, resolving the contradiction between detection coverage and image detail quality.
Solution Approach 2:
The patent uses an autoencoder to create a reconstructed copy of the input image block. By comparing the original image block with its reconstructed copy, the system can detect defects without altering the original image resolution, thus maintaining image detail quality while achieving comprehensive detection coverage.
2Productivity
If traditional defect detection methods are used, then the processing speed is maintained, but the detection accuracy deteriorates due to image detail loss
Solution Approach 1:
The patent pre-trains the autoencoder model using normal product images before actual defect detection. This preliminary action enables the model to learn normal product features in advance, so during detection it can quickly compare actual images against the learned normal patterns, achieving both high processing speed and high detection accuracy without losing image details.
Data Source
AI summary
A method for detecting defects in appearance of a product from images thereof, applied in an electronic device, obtains positive sample images, negative sample images, and product sample images, divides the product sample images into input image blocks, and inputs the input image blocks into a pre-trained autoencoder to obtain reconstructed image blocks. The electronic device determines corresponding pixel points in the input image blocks, and corresponding pixel difference values, and generates feature connection regions of each input image block according to the positive sample images and the pixel difference values. The electronic device generates a first threshold, selects target regions from the feature connection regions and the first threshold, and generates a second threshold. The electronic device further determines a detection result of a product sample in the product sample image according to an area of the target area and the second threshold.