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.

Official implementation of SuperSimpleNet [ICPR 2024, JIMS 2025]

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

Inspection of Power Line Assets Dataset (InsPLAD)

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

[ICSE'24 Industry Challenge Track] "ReposVul: A Repository-Level High-Quality Vulnerability Dataset".

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.

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.

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.

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

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.

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

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].

Textile defect detection using OpenCVSharp

We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.

TFT-LCD defects detecter based on the improved saliency model

MATLAB code and data for "Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection", Soft Computing 2018

The re-labeled NRSD-MN dataset, AMFF-YOLOX source code and paper "AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection".

Lithography defect prediction for microchip manufacturing optimization with machine learning model

Images to Classify Defective and Non-Defective Railway Track

Imaging system for analyzing defects of semiconductor wafers and chips

[ISSN 0168-1699, COMPUT ELECTRON AGR 2024] FastSegFormer: A knowledge distillation-based method for real-time semantic segmentation of surface defects in navel oranges.

Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. To address these limitations, we investigate a computer vision‐based approach that employs SIFT keypoint matching on collected images of defects against a pre-existing reconstructed 3D point cloud of the bridge. We also investigate methods of reducing computation time with ML-based and conventional CV methods of segmentation to eliminate redundant keypoints. Our project successfully localizes the defect images and achieves a savings in runtime from filtering keypoints.

基于 LangChain1.0和DeepAgents的代码优化Agent