Abstract
The importance of an automated defect inspection system has been increasing in the manufacturing industries. Various products to be examined have periodic textures. Among image-based inspection systems, it is common that supervised defect segmentation requires a great number of defect images with their own region-level labels; however, it is difficult to prepare sufficient training data. Because most products are of normal quality, it is difficult to obtain images of product defects. Pixel-wise annotation for semantic segmentation tasks is an exhausting and time-consuming process. To solve these problems, we propose a weakly-supervised defect segmentation framework for defect images with periodic textures and a data augmentation process using generative adversarial networks. With only image-level labeling, the proposed segmentation framework translates a defect image into its defect-free version, called a golden template, using CycleGAN and then segments the defects by comparing the two images. The proposed augmentation process creates whole new synthetic defect images from real defect images to obtain sufficient data. Furthermore, synthetic non-defect images are generated even from real defect images through the augmentation process. The experimental results demonstrate that the proposed framework with data augmentation outperforms an existing weakly-supervised method and shows remarkable results comparable to those of supervised segmentation methods.
Highlights
Most manufacturing industries have aimed to provide their clientele with defect-free products to enhance their corporate competitiveness
We propose an image-based defect segmentation framework for periodic texture images using GAN-based golden template generation and data augmentation process
The proposed data augmentation allows the golden template generator to produce more plausible results. This suggests that our data augmentation scheme improves the generalization performance of the golden template generation
Summary
Most manufacturing industries have aimed to provide their clientele with defect-free products to enhance their corporate competitiveness. In order to detect defects in periodic patterns, various methods utilizing image processing techniques have been introduced: template-based, filter-based, and statistical methods [8]. We propose an image-based defect segmentation framework for periodic texture images using GAN-based golden template generation and data augmentation process. The proposed framework generates the golden template of the input image and segments defects in a pixel-wise manner using simple post-processing. Cao et al [33] introduced a nickel foam surface defect detection method using multi-scale block local binary patterns (MB-LBP) They utilized a non-subsampled contourlet transform (NSCT) to extract multi-scale texture characteristics. This study is similar to our proposed framework in terms of the non-defect image generation using CycleGAN; the total loss function used in the study was inadequate to make a golden template well for periodic patterns. The loss is often auxiliary in other CycleGAN applications, in this work at least, it is crucial to the golden template generation from the perspective that the periodicity of the pattern must remain
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