Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

Camouflaged Object Detection, CVPR 2020 (Oral)

Official PyTorch implementation for "Mixed supervision for surface-defect detection: from weakly to fully supervised learning"

Concealed Object Detection (SINet-V2, IEEE TPAMI 2022). Code is implemented by PyTorch/Jittor frameworks.

Visual Defect Detection on Boiler Water Wall Tube Using Small Dataset

👷胶囊表面缺陷检测withTensorflow，主要检测了凹陷和缺失部分，涉及到GPU加速

基于RetinaFace的目标检测方法，适用于人脸、缺陷、小目标、行人等

This project is about detecting defects on steel surface using Unet. The dataset used for this project is the NEU-DET database.

Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper Award.

Inspection of Power Line Assets Dataset (InsPLAD)

Official pytorch implementation of the paper: "A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection"

Detect Defects in Products from their Images using Amazon SageMaker

This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.

[ICSE 2024 Industry Challenge Track] Official implementation of "ReposVul: A Repository-Level High-Quality Vulnerability Dataset".

This github repository contains the sample code and exercises of btp-ai-sustainability-bootcamp, which showcases how to build Intelligence and Sustainability into Your Solutions on SAP Business Technology Platform with SAP AI Core and SAP Analytics Cloud for Planning.

Textile defect detection using OpenCVSharp

Official PyTorch implementation of the paper "Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images", IEEE Transactions on Instrumentation and Measurement (TIM) 2024. CSBSR is an advanced version of our previous work CSSR [MVA'21].

[ICPR 2024] Official implementation of SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

[ECCV 2024] Official Implementation of An Incremental Unified Framework for Small Defect Inspection

本项目实现了一种基于 VAE-CycleGAN 的图像重建无监督缺陷检测算法。该算法结合了变分自编码器 (VAE) 和 CycleGAN 的优势，无需标注数据即可检测图像中的缺陷/异常。This project implements an unsupervised defect detection algorithm for image reconstruction based on VAE-CycleGAN. This algorithm combines the advantages of variational autoencoders (VAE) and CycleGAN to detect defects in images without any supervision.