Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection

• URL: http://arxiv.org/abs/2103.15158v1
• Date: Sun, 28 Mar 2021 15:53:34 GMT
• Title: Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
• Authors: Gongjie Zhang, Kaiwen Cui, Tzu-Yi Hung, Shijian Lu
• Abstract summary: Defect-GAN is an automated defect synthesis network that generates realistic and diverse defect samples. It learns through defacement and restoration processes, where the defacement generates defects on normal surface images. It can also mimic variations of defects and offer flexible control over the locations and categories of the generated defects.
• Score: 34.699695525216185
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automated defect inspection is critical for effective and efficient maintenance, repair, and operations in advanced manufacturing. On the other hand, automated defect inspection is often constrained by the lack of defect samples, especially when we adopt deep neural networks for this task. This paper presents Defect-GAN, an automated defect synthesis network that generates realistic and diverse defect samples for training accurate and robust defect inspection networks. Defect-GAN learns through defacement and restoration processes, where the defacement generates defects on normal surface images while the restoration removes defects to generate normal images. It employs a novel compositional layer-based architecture for generating realistic defects within various image backgrounds with different textures and appearances. It can also mimic the stochastic variations of defects and offer flexible control over the locations and categories of the generated defects within the image background. Extensive experiments show that Defect-GAN is capable of synthesizing various defects with superior diversity and fidelity. In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.

Related papers

• Few-shot Defect Image Generation based on Consistency Modeling [1.8029094254659288]
  DefectDiffu is a novel text-guided diffusion method to model defect consistency across multiple products. We show that DefectDiffu surpasses state-of-the-art methods in terms of generation quality and diversity.
  arXiv  Detail & Related papers  (2024-08-01T08:29:42Z)
• Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics [27.03052142039447]
  We introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. We also introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images.
  arXiv  Detail & Related papers  (2023-10-26T11:23:24Z)
• CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation [73.02218479926469]
  We propose a transformer network with multi-stage CNN feature injection for surface defect segmentation. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects.
  arXiv  Detail & Related papers  (2023-09-22T06:12:02Z)
• Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation [19.018561017953957]
  We propose the first defect image generation method in the challenging few-shot cases. Our method consists of two training stages. First, we train a data-efficient StyleGAN2 on defect-free images as the backbone. Second, we attach defect-aware residual blocks to the backbone, which learn to produce reasonable defect masks.
  arXiv  Detail & Related papers  (2023-03-04T11:43:08Z)
• Towards Robust Blind Face Restoration with Codebook Lookup Transformer [94.48731935629066]
  Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance. We show that a learned discrete codebook prior in a small proxy space cast blind face restoration as a code prediction task. We propose a Transformer-based prediction network, named CodeFormer, to model global composition and context of the low-quality faces.
  arXiv  Detail & Related papers  (2022-06-22T17:58:01Z)
• Improving robustness against common corruptions with frequency biased models [112.65717928060195]
  unseen image corruptions can cause a surprisingly large drop in performance. Image corruption types have different characteristics in the frequency spectrum and would benefit from a targeted type of data augmentation. We propose a new regularization scheme that minimizes the total variation (TV) of convolution feature-maps to increase high-frequency robustness.
  arXiv  Detail & Related papers  (2021-03-30T10:44:50Z)
• Automatic Defect Detection of Print Fabric Using Convolutional Neural Network [0.0]
  There are different contemporary research on automatic defect detection systems using image processing and machine learning techniques. Researchers have also been able to establish real-time defect detection system during weaving. This research has fulfilled this gap by developing a print fabric database and implementing deep convolutional neural network.
  arXiv  Detail & Related papers  (2021-01-03T20:56:56Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)
• Uneven illumination surface defects inspection based on convolutional neural network [4.475821533240529]
  This paper proposes a method for detecting surface image defects based on convolutional neural network. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image.
  arXiv  Detail & Related papers  (2019-05-16T12:18:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.