Defect Detection
55 papers with code • 5 benchmarks • 8 datasets
For automatic detection of surface defects in various products
Benchmarks
These leaderboards are used to track progress in Defect Detection
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Datasets
CodeXGLUE
KolektorSDD
KolektorSDD2
DAGM2007
Deep PCB
Most implemented papers
Deep Learning Based Steel Pipe Weld Defect Detection
huangyebiaoke/steel-pipe-weld-defect-detection
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Steel pipes are widely used in high-risk and high-pressure scenarios such as oil, chemical, natural gas, shale gas, etc.
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection
Periodic inspections are necessary to keep railroad tracks in state of good repair and prevent train accidents.
Learning to Detect Multiple Photographic Defects
ningyu1991/DefectDetection
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Our new dataset enables us to formulate the problem as a multi-task learning problem and train a multi-column deep convolutional neural network (CNN) to simultaneously predict the severity of all the defects.
Surface Defect Saliency of Magnetic Tile
Vision-based detection on surface defects has long postulated in the magnetic tile automation process.
Online PCB Defect Detector On A New PCB Defect Dataset
To train the deep model, a dataset is established, namely DeepPCB, which contains 1, 500 image pairs with annotations including positions of 6 common types of PCB defects.
DefectNET: multi-class fault detection on highly-imbalanced datasets
As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data.
An Evalutation of Programming Language Models' performance on Software Defect Detection
hiroto-takatoshi/ProgLMBug
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Language models for source code are specified for tasks in the software engineering field.
Coverage Guided Testing for Recurrent Neural Networks
The test metrics and test case generation algorithm are implemented into a tool TestRNN, which is then evaluated on a set of LSTM benchmarks.
Semi-supervised Anomaly Detection using AutoEncoders
msminhas93/anomaly-detection-using-autoencoders
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Journal of Computational Vision and Imaging Systems 2020
But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem.
Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
To end this, we propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT).
