Product Defect Detection via Autoencoder Image Segmentation
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Solution Overview
Problem
Existing methods for detecting defects in products face challenges in accurately identifying minor defects or defects that are position-specific, leading to low detection accuracy.
Innovation Solution
A computer device-based method involving an acquisition module to capture product images, an automatic encoder for reconstructing images, and an execution module to segment and calculate mean square errors between original and reconstructed images to determine defect presence, enabling precise defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image-based defect detection methods are used, then the detection process is simple, but the accuracy in detecting minor or position-specific defects is low
Solution Approach 1:
The patent segments the product image into multiple blocks and processes each block independently through the autoencoder. This segmentation allows the system to focus computational resources on specific regions, improving defect detection accuracy while managing system complexity through modular processing
Solution Approach 2:
The patent introduces an autoencoder as an intermediary component between image acquisition and defect analysis. The autoencoder reconstructs images and generates feature maps that highlight defects, acting as a mediator that transforms raw images into enhanced defect representations without requiring complex manual processing
2Reliability
If traditional defect detection methods are used, then the system is easy to operate, but it cannot effectively identify minor defects or defects in certain positions
Solution Approach 1:
The patent implements feedback through the autoencoder's reconstruction process. The system compares original images with reconstructed images, using the difference (feature maps) as feedback to identify defects. This feedback mechanism continuously refines defect detection by highlighting discrepancies between expected and actual image features
Solution Approach 2:
The patent performs preliminary image reconstruction and feature extraction before final defect analysis. By pre-processing images through the autoencoder to generate enhanced feature maps, the system prepares defect-highlighted representations in advance, making the subsequent defect identification more reliable and automated
Data Source
AI summary
A method for detecting defects in product applied in a computer device inputs an image of a product under test to an automatic encoder to obtain a reconstructed image, and the image is segmented into N image blocks and the reconstructed image is segmented into N image blocks. The computer device associates each of the N testing blocks with one reconstructed blocks according to positions of the N testing blocks in the image and positions of the N reconstructed blocks in the reconstructed image. The computer device further calculates mean square errors between each of the N testing blocks and each of the N reconstructed blocks, and associates each mean square error with each of the N testing blocks, whether the product has defects being determined based on the mean square errors corresponding to each of the N testing blocks.