Defect Detection
73 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
Datasets
CodeXGLUE
KolektorSDD2
KolektorSDD
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
TooTouch/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.
