Automatic Fabric Defect Detection Method Using PRAN-Net

Peng, Peiran; Wang, Ying; Hao, Chuanyong; Zhu, Zhizhong; Liu, Tong; Zhou, Weihu

doi:10.3390/app10238434

Cited by 21 publications

(13 citation statements)

References 21 publications

(27 reference statements)

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“…If the video captured by industrial camera is input, this model will cause a significant delay. The detection speed shows in [19] and [20] is close to 19 images per second. However, they use the grey level image with shape (128,128).…”

Section: Experiments and Resultsmentioning

confidence: 71%

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Fabric Defect Detection based on Improved Faster RCNN

He¹,

Zhang²,

Huang³

et al. 2021

IJAIA

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In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

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Section: Experiments and Resultsmentioning

confidence: 71%

“…Therefore, it is questionable whether this algorithm can be used for real-time detection. Peng [20] introduced an improved prior anchor network to enhance the Faster RCNN performance. Peng' group used amount of industrial cloth data to train the network, and achieve 98.6% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection based on Improved Faster RCNN

He¹,

Zhang²,

Huang³

et al. 2021

IJAIA

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show abstract

“…It has been shown that it is possible to train supervised [15,17,19,[27][28][29] as well as semisupervised [11] fabric defect detection methods on multi-fabric datasets. However, it has also been shown that the proposed algorithms generalize poorly to fabrics unseen during training [20,21].…”

Section: Post Hoc Adaptation Techniquesmentioning

confidence: 99%

Increasing the Generalization of Supervised Fabric Anomaly Detection Methods to Unseen Fabrics

Rippel

Zwinge

Merhof

2022

Sensors

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Fabric ad tries to detect anomalies (i.e., defects) in fabrics, and fabric ad approaches are continuously improved with respect to their ad performance. However, developed solutions are known to generalize poorly to previously unseen fabrics, posing a crucial limitation to their applicability. Moreover, current research focuses on adapting converged models to previously unseen fabrics in a post hoc manner, rather than training models that generalize better in the first place. In our work, we explore this potential for the first time. Specifically, we propose that previously unseen fabrics can be regarded as shifts in the underlying data distribution. We therefore argue that factors which reportedly improve a model’s resistance to distribution shifts should also improve the performance of supervised fabric ad methods on unseen fabrics. Hence, we assess the potential benefits of: (I) vrm techniques adapted to the fabric ad use-case, (II) different loss functions, (III) ImageNet pre-training, (IV) dataset diversity, and (V) model architecture as well as model complexity. The subsequently performed large-scale analysis reveals that (I) only the vrm technique, AugMix, consistently improves performance on unseen fabrics; (II) hsc outperforms other loss functions when combined with AugMix and (III) ImageNet pre-training, which is already beneficial on its own; (IV) increasing dataset diversity improves performance on unseen fabrics; and (V) architectures with better ImageNet performance also perform better on unseen fabrics, yet the same does not hold for more complex models. Notably, the results show that not all factors and techniques which reportedly improve a model’s resistance to distribution shifts in natural images also improve the generalization of supervised fabric ad methods to unseen fabrics, demonstrating the necessity of our work. Additionally, we also assess whether the performance gains of models which generalize better propagate to post hoc adaptation methods and show this to be the case. Since no suitable fabric dataset was publicly available at the time of this work, we acquired our own fabric dataset, called OLP, as the basis for the above experiments. OLP consists of 38 complex, patterned fabrics, more than 6400 images in total, and is made publicly available.

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“…Peng et al put forward a detection algorithm called Priori Anchor Convolutional Neural Network (PRAN-Net) to fix this problem. Feature Pyramid Network (FPN) is utilized to selected multiscale feature maps and then sparse priori anchors are generated based on ground truth boxes [83].…”

Section: Deep Learning Algorithmsmentioning

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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Automatic Fabric Defect Detection Method Using PRAN-Net

Cited by 21 publications

References 21 publications

Fabric Defect Detection based on Improved Faster RCNN

Fabric Defect Detection based on Improved Faster RCNN

Increasing the Generalization of Supervised Fabric Anomaly Detection Methods to Unseen Fabrics

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

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