Journal ArticleDOI
Defect Attention Template Generation CycleGAN for Weakly Supervised Surface Defect Segmentation
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TLDR
Wang et al. as mentioned in this paper proposed a weakly supervised defect segmentation method based on the dynamic templates generated by an improved cycle-consistent generative adversarial network (CycleGAN) trained by image-level annotations.About:
This article is published in Pattern Recognition.The article was published on 2022-03-01. It has received 6 citations till now. The article focuses on the topics: Discriminator & Segmentation.read more
Citations
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Journal ArticleDOI
A New Cycle-consistent Adversarial Networks With Attention Mechanism for Surface Defect Classification With Small Samples
TL;DR: Wang et al. as discussed by the authors proposed a new cycle-consistent adversarial networks with attention mechanism (AttenCGAN), which is used for synthesizing defect samples to enlarge the samples volume and adopted the attention mechanism for feature enhancement by finding the discriminative parts of the samples and enlarging the differences among the samples.
Journal ArticleDOI
Data Augmentation by an Additional Self-Supervised CycleGAN-Based for Shadowed Pavement Detection
TL;DR: Wang et al. as discussed by the authors proposed an improved shadow generation network, named Texture Self-Supervised CycleGAN (CycleGAN-TSS), which can improve the effect of generation and can be used to augment the band of shadows of pavement cracks.
Journal ArticleDOI
Learning position information from attention: End-to-end weakly supervised crack segmentation with GANs
Ye Liu,Jun Chen,Jianlong Hou +2 more
TL;DR: RepairerGAN as mentioned in this paper decouples the image-to-image translation model of two different image domains into a semantic translation module and a position extraction module and uses the attention mechanism to extract the crack position information as the segmentation result.
Journal ArticleDOI
Generating Defective Epoxy Drop Images for Die Attachment in Integrated Circuit Manufacturing via Enhanced Loss Function CycleGAN
TL;DR: In this article , a GAN was used to generate synthetic defective epoxy drop images as a data augmentation approach so that vision-based deep neural networks can be trained or tested using such images.
Journal ArticleDOI
Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey
TL;DR: In this paper , the authors reviewed more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defect inspection systems, deployed in various industrial applications.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Journal ArticleDOI
Squeeze-and-Excitation Networks
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Proceedings ArticleDOI
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
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Semi-supervised learning with GAN for automatic defect detection from images
Gaowei Zhang,Yue Pan,Limao Zhang +2 more