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   Articles with "defect segmentation" as a keyword



SAIT: Harnessing Sparse Annotations and Intrinsic Tasks for Semisupervised Aeroengine Defect Segmentation

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recommendations! Published in 2024 at "IEEE Transactions on Industrial Informatics"

DOI: 10.1109/tii.2024.3396550

Abstract: In aeroengine maintenance, endoscopic imaging serves as a crucial tool for detecting blade defects and evolves toward intelligence driven by computer vision technology. Currently, supervised-learning-based defect segmentation methods mainly rely on extensive pixel-level annotations, making… read more here.

Keywords: pixel level; segmentation; harnessing sparse; sait harnessing ... See more keywords

Unsupervised Defect Segmentation of Magnetic Tile Based on Attention Enhanced Flexible U-Net

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recommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2022.3170989

Abstract: Surface defect inspection is necessary for the production of magnetic tiles. Automated inspection based on machine vision and artificial intelligence can greatly improve the efficiency. However, collecting sufficient defect samples and marking them require a… read more here.

Keywords: tile; unsupervised defect; defect segmentation; based attention ... See more keywords

Machine Vision-Based Lightweight Multiscale Automatic Defect Segmentation Network for MLCC

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recommendations! Published in 2024 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2024.3415777

Abstract: Defect detection in multilayer ceramic capacitors (MLCCs) is critical for ensuring the production quality. However, surface defect detection in MLCC faces challenges, such as multiscale defects, indistinct boundaries, and limited availability of defect samples. To… read more here.

Keywords: segmentation; machine vision; defect segmentation; lightweight multiscale ... See more keywords

PUNet: Printed Fabric Defect Segmentation Method Based on UNet

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recommendations! Published in 2025 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2025.3550640

Abstract: The contrast between printed defects and background texture is extremely low, and the defects are concealed within the patterns and colors, most existing detection methods are often unable to achieve promising results. This article proposes… read more here.

Keywords: based unet; fabric defect; printed fabric; defect segmentation ... See more keywords

Semi-Supervised Surface Defect Segmentation via Contrastive Learning

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recommendations! Published in 2025 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2025.3612555

Abstract: Quality inspection of industrial products is a critical task to ensure that products meet the required standards. Although deep learning-based detection models have achieved significant progress in recent years, the limited availability of samples in… read more here.

Keywords: loss; segmentation; semi supervised; defect segmentation ... See more keywords

FDTransUnet: An aluminum surface defect segmentation model based on feature differentiation

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recommendations! Published in 2025 at "PLOS One"

DOI: 10.1371/journal.pone.0320060

Abstract: Aiming at the current problems in the field of industrial defect segmentation, such as difficulty of obtaining a large number of defect samples, low recognition accuracy and lack of segmentation accuracy, a surface defect segmentation… read more here.

Keywords: surface defect; segmentation; feature differentiation; defect segmentation ... See more keywords

Steel surface defect segmentation with SME-DeeplabV3+

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recommendations! Published in 2025 at "PLOS One"

DOI: 10.1371/journal.pone.0329628

Abstract: Accurate segmentation of steel surface defects is crucial for ensuring steel quality. This paper presents a steel surface defect segmentation method based on SME-DeepLabV3+ to improve the accuracy and efficiency of segmentation. First, StarNet is… read more here.

Keywords: surface defect; segmentation; steel surface; steel ... See more keywords