There are 33 repositories under defect-detection topic.
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"
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.
IDDM (Industrial, landscape, animate...), support DDPM, DDIM, PLMS, webui and multi-GPU distributed training. Pytorch实现,生成模型,扩散模型,分布式训练
Multi-label defect detection for Solar Cells from Electroluminescence images of the modules, using Deep Learning
Official pytorch implementation of the paper: "A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection"
Inspection of Power Line Assets Dataset (InsPLAD)
Detect Defects in Products from their Images using Amazon SageMaker
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
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.
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"
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.
Imaging system for analyzing defects of semiconductor wafers and chips
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".
Classification of automotive parts as defective and non-defective with transfer learning.
Use ResNet50 deep learning model to predict defects in steel and visually localize the defect using Res-UNET model class
[Computers and Electronics in Agriculture 2024] FastSegFormer: A knowledge distillation-based method for real-time semantic segmentation of surface defects in navel oranges.
Fabric Stain Detection System based on YOLO algorithm
We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.
SegDecNet++: an official PyTorch implementation for "Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network" paper
This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.
Lithography defect prediction for microchip manufacturing optimization with machine learning model
Automate the inspection of conveyor belts by detecting tracking variances and surface defects (tears and stains).
Detection and Segmentation in Powder Spreading Process of Magnetic Material Additive Manufacturing