Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

Wang, Zhen; Jing, Junfeng

doi:10.1109/access.2020.3021189

Cited by 25 publications

(15 citation statements)

References 35 publications

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“…We used three criteria to analyze the model 22 quantitatively: pixel accuracy (PA), mean pixel accuracy (MPA), and mean intersection over union (MIoU). As shown in Table I V, our model was superior to the other three methods in MPA and MIoU.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Real-Time Fabric Defect Segmentation Based on Convolutional Neural Network

Wang

Zhang

et al. 2021

AATCC Journal of Research

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Automated visual inspection for quality control has widely-used deep convolutional neural networks (CNNs) in fabric defect detection. Most of the research on defect detection only focuses on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. In this study, we propose a highly efficient deep learning-based method for pixel-level fabric defect classification algorithm based on a CNN. We started with the ShuffleNet V2 feature extractor, added five deconvolution layers as the decoder, and used a resize bilinear to produce the segmentation mask. To solve the sample imbalance problem, we used an improved loss function to guide network learning. We evaluated our model on the fabric defect data set. The proposed model outperformed the existing image segmentation models in both model efficiency and segmentation accuracy.

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Section: Experiments and Discussionmentioning

confidence: 99%

Real-Time Fabric Defect Segmentation Based on Convolutional Neural Network

Wang

Zhang

et al. 2021

AATCC Journal of Research

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“…erefore, many researchers employ semisupervised and unsupervised learning algorithms for the detection [115]. In addition, some studies utilize nondefect image data and synthetic defective image data generated by using defect characteristics based on expert knowledge [91].…”

Section: Datasetmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

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Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

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“…(1) A hybrid welding fault diagnosis scheme based on ACGAN [43,44] (auxiliary classifier generative adversarial networks) and CNN [45,46] model has been proposed. Fake data are generated by the ACGAN generator using real data, and the CNN classifier is trained with both fake data and real data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Fan

Peng

Zhou

et al. 2021

Sensors

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Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.

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Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

Cited by 25 publications

References 35 publications

Real-Time Fabric Defect Segmentation Based on Convolutional Neural Network

Real-Time Fabric Defect Segmentation Based on Convolutional Neural Network

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

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