Defect Detection
56 papers with code • 5 benchmarks • 8 datasets
For automatic detection of surface defects in various products
Benchmarks
These leaderboards are used to track progress in Defect Detection
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Datasets
CodeXGLUE
KolektorSDD
KolektorSDD2
DAGM2007
Deep PCB
Most implemented papers
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
salesforce/codet5
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EMNLP 2021
We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
areylng/MemSeg
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By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner.
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
To achieve a high robustness and performance we exploit multiple transformations in training and evaluation.
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
vitjanz/draem
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Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.
DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Runinho/pytorch-cutpaste
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CVPR 2021
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.
Mixed supervision for surface-defect detection: from weakly to fully supervised learning
vicoslab/mixed-segdec-net-comind2021
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We also show that mixed supervision with only a handful of fully annotated samples added to weakly labelled training images can result in performance comparable to the fully supervised model's performance but at a significantly lower annotation cost.
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection
shellysheynin/HTDG-model
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ICCV 2021
We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec.
