广东工业智造大赛--布匹瑕疵检测复赛代码

Implementation of the paper: Defect Detection in Plain Weave Fabrics by Yarn Tracking and Fully Convolutional Networks

AIFT2019-Real-time fabric defect segmentation based on convolutional neural network

A weakly annotated fabric defect dataset. Contains 24 512x512 images. Every image has a 512x512 mask. The mask has one rectangular bounding box covering the entire defect but has pixels of fabric without defect. This makes the defect detection task challenging.

Fabric defect detection based on computer vision

Introduction of new dataset for unsupervised fabric defect detection

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山东大学（威海）2022年机器学习项目———布匹缺陷检测

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The aim of this project is to develop a model capable of detecting fabric defection.

Fabric Defect Detection with OpenCV

Context In the context of textile fabric, rare anomaly can occurs, hence compromising the quality of the tissues. In order to avoid that in some scenario, it is crucial to detect the defect. This dataset is for educational purposes Content Image size: 32x32 or 64x64 classes: ['good', 'color', 'cut', 'hole', 'thread', 'metal contamination'] rotations: 8 different rotations in [0, 20, 40, 60, 80, 100, 120, 140] Given an image size, a train and test dataset are available with randomly generated patches. Source images from the train and test are non-overlapping different tasks are possible: classification of the classes type classification of angles using only "good" images and testing of other classes texture representation learning / self-supervised learning Acknowledgements Based on the public dataset by the MVTec company Paul Bergmann, Michael Fuser, David Sattlegger, Carsten Steger. MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019 Inspiration the main goal of this dataset is to explore self-supervised learning on texture images in order to solve anomaly detection problems and learn a robust representation of texture in lieu of traditional image processing features (e.g. glcm, gabor,….)

This is a fabric defect detection methed based on AE-BP. Here's the open source code for the auto encoder.

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This research proposes a modified network YOLOv5 model to determine the best model for jeans fabric defect detection.

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A detection model for fabric defects based on deep Cascade R-CNN is proposed, focusing on the substantive difficulties such as the increase of hard examples caused by various patterns and varieties of defects.

One-Class Model for Fabric Defect Detection

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An object detection framework for intelligent identification of fabric defects

Fabric Defect Detection using Image Processing method

Small Fabric Database for Defect Detection

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Classifier for detection of defects in clothes

一个基于opencv及大模型智能检测分析的布匹缺陷检测系统

Built a real-time, purely classical computer vision system for fabric defect detection using multi-method analysis (GLCM, FFT, Gabor, statistical variance, background subtraction, and edge–Hough), with IoU-based bounding box fusion for robust localization. Deployed and optimized the pipeline on Jetson Nano for real time defect detection.

[Ali Tianchi] my baseline for Intelligent Detection of Fabric Defects

FabricVision is an AI-driven real-time fabric inspection system integrating camera-based acquisition, edge-level enhancement, ML defect detection, and a centralized QC dashboard. It automates inspection, improves defect accuracy, and provides real-time monitoring, traceability, and decision support.