defect-detection

Camouflaged Object Detection, CVPR 2020 (Oral)

Visual Defect Detection on Boiler Water Wall Tube Using Small Dataset

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

基于RetinaFace的目标检测方法，适用于人脸、缺陷、小目标、行人等

👷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

Concealed Object Detection (SINet-V2, IEEE TPAMI 2021). Code using Jittor Framework is available.

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

Textile defect detection using OpenCVSharp

Multi-label defect detection for Solar Cells from Electroluminescence images of the modules, using Deep Learning

TFT-LCD defects detecter based on the improved saliency model

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.

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.

Classification of automotive parts as defective and non-defective with transfer learning.

[TF 2.x] PaDiM - unofficial tensorflow implementation of the paper 'a Patch Distribution Modeling Framework for Anomaly Detection and Localization'.

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

Collection of methods for the analysis of solar modules

Keep an Eye on Defects Inspection.

Imaging system for analyzing defects of semiconductor wafers and chips

Reinforced Concrete Bridge Defect Detection with Convolutional Neural Networks

Application for searching for defects in plates (Ellipse method and Time-reversal)

Defect Detection and Elliptical Object Localization with DenseNet-169 on subset of DAGM 2007 Defect Dataset

Use ResNet50 deep learning model to predict defects in steel and visually localize the defect using Res-UNET model class

Cork stopper quality analysis and comparison vision system using RGB shadow decomposition for industrial application.

A defect detect method based on Kalman filter

Defect detection prototype and baseline for X4Vision

Extracting the G-code cloud points and saving them in CSV. Simultaneously background of the images are removed and compared with the G-code point clouds, thus detecting errors in the 3D printed component.

Detecting defective apples

multi-view Cards based Requirement Acquisition and Modelling tool