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Please refer to this page.
Considering the increasing number of papers in this field, we roughly summarize some articles and put them into the following categories (they are still listed in this document):
Note that all unpublished arXiv papers are not included in the leaderboard of performance.
- Segmentation-Based Deep-Learning Approach for Surface-Defect (SCI)[paper][code]
- Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows [paper]
- Deep Learning for Anomaly Detection: A Survey [paper][code]
Earlier ArXiv Papers
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Inspection of Imprint Defects in Stamped Metal Surfaces using Deep Learning and Tracking (IEEE-TIE)[paper]
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[RPAN] Deep Learning-based Solar-Cell Manufacturing Defect Detection with Complementary Attention Network (Network-TII)[paper]
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[PGA-Net] PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection (IEEE-TII)[paper]
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Deep-Learning-Based Small Surface Defect Detection via an Exaggerated Local Variation-Based Generative Adversarial Network (IEEE-TII)[paper]
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[ESP] Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors (IEEE-TII)[paper]
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[NDT] Multiview Learning for Subsurface Defect Detection in Composite Products: A Challenge on Thermographic Data Analysis (IEEE-TII)[paper]
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[ERSCD] A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform (IEEE-TII)[paper]
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[WGANs] A learning-based approach for surface defect detection using small image datasets [paper]
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[Faster VG-RCNN] Detecting textile micro-defects: A novel and efficient method based on visual gain mechanism [paper]
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An Efficient Convolutional Neural Network Model Based on Object-Level Attention Mechanism for Casting Defect Detection on Radiography Images (IEEE-TIE)[paper]
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[GAN] Multistage GAN for Fabric Defect Detection (IEEE-TIP)[paper]
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A simulation-based few samples learning method for surface defect segmentation [paper]
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A Generic Semi-Supervised Deep Learning-Based Approach for Automated Surface Inspection (IEEE-Access)[paper]
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A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision (Inf Fusion)[paper]
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A Steel Surface Defect Recognition Algorithm Based on Improved Deep Learning Network Model Using Feature Visualization and Quality Evaluation (IEEE-Access)[paper]
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Automated defect inspection system for metal surfaces based on deep learning and data augmentation (Inf Fusion)[paper]
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[CADN] CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection (Inf Fusion)[paper]
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[PDDNet] Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology (IEEE-TITS)[paper]
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Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network (IEEE Access)[paper]
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Detection of PV Solar Panel Surface Defects using Transfer Learning of the Deep Convolutional Neural Networks (ASET)[paper]
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Electronic Product Surface Defect Detection Based on a MSSD Network (IEEE-ITNEC)[paper]
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Research on Defect Detection Method for Steel Metal Surface based on Deep Learning (IEEE-ITOEC)[paper]
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Surface damage detection for steel wire ropes using deep learning and computer vision techniques [paper]
- [ESP] Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors (IEEE-TII)[paper]
- [CODEBRIM] Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset(CVPR-2019)[paper]
- [SDD-CNN] applsci-SDD-CNN Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection(Applied Sciences) [paper]
- An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features (IEEE-TIM)[paper]
- A New Self-Reference Image Decomposition Algorithm for Strip Steel Surface Defect Detection(IEEE-TIM) [paper]
- Automatic fabric defect detection with a wide-and-compact network [paper]
- AnomalyNet: An Anomaly Detection Network for Video Surveillance (IEEE-TIFS)[paper]
- Anomaly Detection in Images (CVPR-2019)[paper]
- [CTFM] A Coarse-to-Fine Model for Rail Surface Defect Detection (IEEE-TIM)[paper]
- [MIL] Real-world Anomaly Detection in Surveillance Videos (CVPR-2018)[paper][code]
The section is being continually updated. Note that some values have superscript, which indicates their source.