Defect Detection

53 papers with code • 5 benchmarks • 8 datasets

For automatic detection of surface defects in various products

Most implemented papers

Segmentation-Based Deep-Learning Approach for Surface-Defect Detection

skokec/segdec-net-jim2019 20 Mar 2019

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 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.

MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities

areylng/MemSeg 2 May 2022

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

marco-rudolph/differnet 28 Aug 2020

To achieve a high robustness and performance we exploit multiple transformations in training and evaluation.

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

microsoft/CodeXGLUE 9 Feb 2021

Benchmark datasets have a significant impact on accelerating research in programming language tasks.

DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection

vitjanz/draem 17 Aug 2021

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

vitjanz/draem ICCV 2021

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 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 13 Apr 2021

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 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.