Papers Defect Detection
“Defect Detection” 태그가 달린 논문 394편 · 필터 해제
Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning
Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good dete…
Defect DetectionGraph Convolutions Enrich the Self-Attention in Transformers!
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the c…
Clone DetectionCode ClassificationDefect Detection+5How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the assumption prevails that large training…
Anomaly DetectionDefect DetectionPneumonia DetectionUnsupervised Anomaly DetectionDeveloping a Resource-Constraint EdgeAI model for Surface Defect Detection
Resource constraints have restricted several EdgeAI applications to machine learning inference approaches, where models are trained on the cloud and deployed to the edge device. This poses challenges such as bandwidth, l…
Defect DetectionUnsupervised textile defect detection using convolutional neural networks
In this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. I…
Anomaly DetectionDefect Detectionfeature selectionA Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN
Accurate Defect detection is crucial for ensuring the trustworthiness of intelligent railway systems. Current approaches rely on single deep-learning models, like CNNs, which employ a large amount of data to capture unde…
Defect DetectionEnsemble LearningTransfer LearningImproved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy
In semiconductor manufacturing, lithography has often been the manufacturing step defining the smallest possible pattern dimensions. In recent years, progress has been made towards high-NA (Numerical Aperture) EUVL (Extr…
Defect DetectionNAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search
Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable ef…
Defect DetectionNeural Architecture SearchSegment Anything in Defect Detection
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities. However, challenges such as low resolution, high noise, and uneven hea…
Defect DetectionDeepInspect: An AI-Powered Defect Detection for Manufacturing Industries
Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This techno…
Defect Detection3-Dimensional residual neural architecture search for ultrasonic defect detection
This study presents a deep learning methodology using 3-dimensional (3D) convolutional neural networks to detect defects in carbon fiber reinforced polymer composites through volumetric ultrasonic testing data. Acquiring…
Defect DetectionDimensionality ReductionNeural Architecture SearchSynthetic Data Generation$e^{\text{RPCA}}$: Robust Principal Component Analysis for Exponential Family Distributions
Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious…
Defect DetectionDeep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffractio…
Defect DetectionImage-to-Image TranslationSuper-ResolutionObservational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers
This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of researc…
ClassificationDefect DetectionSurveyDefect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches
Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve producti…
Defect DetectionModel OptimizationTransfer LearningA Discrepancy Aware Framework for Robust Anomaly Detection
Defect detection is a critical research area in artificial intelligence. Recently, synthetic data-based self-supervised learning has shown great potential on this task. Although many sophisticated synthesizing strategies…
Anomaly DetectionDecoderDefect DetectionKnowledge Distillation+1Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation
As the globalization of semiconductor design and manufacturing processes continues, the demand for defect detection during integrated circuit fabrication stages is becoming increasingly critical, playing a significant ro…
Defect DetectionImproving Vision Anomaly Detection with the Guidance of Language Modality
Recent years have seen a surge of interest in anomaly detection for tackling industrial defect detection, event detection, etc. However, existing unsupervised anomaly detectors, particularly those for the vision modality…
Anomaly DetectionDefect DetectionEvent DetectionMFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection
Recently, the application of computer vision for anomaly detection has been under attention in several industrial fields. An important example is oil pipeline defect detection. Failure of one oil pipeline can interrupt t…
Anomaly DetectionDefect DetectionImagery Dataset for Condition Monitoring of Synthetic Fibre Ropes
Automatic visual inspection of synthetic fibre ropes (SFRs) is a challenging task in the field of offshore, wind turbine industries, etc. The presence of any defect in SFRs can compromise their structural integrity and p…
Defect Detectionobject-detectionObject Detection