Papers Defect Detection
“Defect Detection” 태그가 달린 논문 394편 · 필터 해제
UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection
Visual anomaly detection aims to learn normality from normal images, but existing approaches are fragmented across various tasks: defect detection, semantic anomaly detection, multi-class anomaly detection, and anomaly c…
Anomaly DetectionDefect DetectionMulti-class Anomaly DetectionMultiple Instance LearningMorphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models
As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions. This …
Defect DetectionAnomaly Detection in Automated Fibre Placement: Learning with Data Limitations
Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However,…
Anomaly DetectionBinary ClassificationDefect DetectionTFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder
Texture is an essential information in image representation, capturing patterns and structures. As a result, texture plays a crucial role in the manufacturing industry and is extensively studied in the fields of computer…
Defect DetectionNatural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review
This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained u…
Clone DetectionCode CompletionCode GenerationCode Summarization+3The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection
The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting. This report introduces the technical details of…
Defect DetectionInstance SegmentationSegmentationSemantic SegmentationLightweight wood panel defect detection method incorporating attention mechanism and feature fusion network
In recent years, deep learning has made significant progress in wood panel defect detection. However, there are still challenges such as low detection , slow detection speed, and difficulties in deploying embedded device…
Defect DetectionExploring the Relationship between Samples and Masks for Robust Defect Localization
Defect detection aims to detect and localize regions out of the normal distribution.Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizabi…
Defect DetectionGenerative Adversarial NetworkPositionFABLE : Fabric Anomaly Detection Automation Process
Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to …
Anomaly DetectionDefect DetectionDomain GeneralizationSpecificityVISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce th…
Defect DetectionInstance SegmentationSemantic SegmentationAtrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effectiv…
Defect DetectionSpecificityReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. However, the features extracted from the …
Anomaly DetectionContrastive LearningDecoderDefect Detection+1Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, n…
Data AugmentationDefect DetectionDomain AdaptationSimulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing
In recent years, laser ultrasonic visualization testing (LUVT) has attracted much attention because of its ability to efficiently perform non-contact ultrasonic non-destructive testing.Despite many success reports of dee…
Data AugmentationDeep LearningDefect DetectionStyle TransferMultimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition
Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) …
Defect DetectionSensor FusionA Study on Deep CNN Structures for Defect Detection From Laser Ultrasonic Visualization Testing Images
The importance of ultrasonic nondestructive testing has been increasing in recent years, and there are high expectations for the potential of laser ultrasonic visualization testing, which combines laser ultrasonic testin…
Defect DetectionObjectobject-detectionObject DetectionQuantifying the effect of X-ray scattering for data generation in real-time defect detection
Background: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional…
Defect DetectionImage GenerationLine DetectionA Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection
Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks…
Deep LearningDefect Detectionobject-detectionObject DetectionThe EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
The use of modern Natural Language Processing (NLP) techniques has shown to be beneficial for software engineering tasks, such as vulnerability detection and type inference. However, training deep NLP models requires sig…
Code ClassificationDefect DetectionMulti-class ClassificationVulnerability DetectionA Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry
The field of industrial defect detection using machine learning and deep learning is a subject of active research. Datasets, also called benchmarks, are used to compare and assess research results. There is a number of d…
Defect Detection