Papers Defect Detection
“Defect Detection” 태그가 달린 논문 394편 · 필터 해제
Water Bottle Defect Detection System Using Convolutional Neural Network
Water bottling is the major task in water and beverage production companies. One of the most concerning issues during the bottling process is to assure Encapsulation quality, such as no cover, high cover, crooked (cu…
Data AugmentationDefect Detectionobject-detectionObject DetectionUnsupervised Visual Defect Detection with Score-Based Generative Model
Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect imag…
Anomaly DetectionDefect DetectionDenoisingNormal Reference Attention and Defective Feature Perception Network for Surface Defect Detection
Visual anomaly detection plays a significant role in the development of industrial automatic product quality inspection. As a result of the utmost imbalance in the amount of normal and abnormal data, growing attention ha…
Anomaly DetectionDefect DetectionDeep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach
In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect i…
Defect DetectionInstance Segmentationobject-detectionObject Detection+1Poison Attack and Defense on Deep Source Code Processing Models
In the software engineering community, deep learning (DL) has recently been applied to many source code processing tasks. Due to the poor interpretability of DL models, their security vulnerabilities require scrutiny. Re…
Clone DetectionCode RepairDefect DetectionAsymmetric Student-Teacher Networks for Industrial Anomaly Detection
Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work discovers previously unknown problems of stu…
3D Anomaly DetectionAnomaly DetectionDefect DetectionDensity Estimation+1Superpixel perception graph neural network for intelligent defect detection of aero-engine blade
Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operatio…
Defect DetectionGraph Neural NetworkRegion ProposalAn Ontology for Defect Detection in Metal Additive Manufacturing
A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monit…
Data IntegrationDecision MakingDefect DetectionDiagnostic+1A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection
Surface defect detection is one of the most essential processes for industrial quality inspection. Deep learning-based surface defect detection methods have shown great potential. However, the well-performed models usual…
Defect DetectionKnowledge DistillationTransfer LearningIntelligent detect for substation insulator defects based on CenterMask
With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing spe…
Defect DetectionAnomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells
Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and seman…
Anomaly DetectionAnomaly SegmentationDefect DetectionSemantic SegmentationQuantum artificial vision for defect detection in manufacturing
In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we…
Defect DetectionDimensionality ReductionClear Memory-Augmented Auto-Encoder for Surface Defect Detection
In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. Specifically, reconstructi…
Anomaly DetectionDefect DetectionEfficient Neural Net Approaches in Metal Casting Defect Detection
One of the most pressing challenges prevalent in the steel manufacturing industry is the identification of surface defects. Early identification of casting defects can help boost performance, including streamlining produ…
Defect DetectionSemiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning
With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and…
Deep LearningDefect DetectionConvolutional Ensembling based Few-Shot Defect Detection Technique
Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general…
Anomaly DetectionDefect DetectionFew-Shot LearningImage-based Detection of Surface Defects in Concrete during Construction
Defects increase the cost and duration of construction projects as they require significant inspection and documentation efforts. Automating defect detection could significantly reduce these efforts. This work focuses on…
Active LearningDefect DetectionInstance SegmentationSemantic SegmentationDSR -- A dual subspace re-projection network for surface anomaly detection
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution a…
Anomaly DetectionAnomaly LocalizationDefect DetectionSupervised Defect Detection+2Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners
Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment d…
Defect DetectionAdaptive Learning for the Resource-Constrained Classification Problem
Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufac…
ClassificationDefect Detection