Fabric Defect Segmentation Method Based on Deep Learning

Huang, Yanqing; Jing, Junfeng; Wang, Zhen

doi:10.1109/tim.2020.3047190

Cited by 71 publications

(41 citation statements)

References 43 publications

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“…They simplified the number of convolution layers and the number of neurons in the full connection layer in the CCN network structure of AlexNet [29] in order to reduce the parameters and fed the samples to it for fabric defect recognition and classification. Y. Huang et al [30] proposed an efficient CNN for defect segmentation and detection of defects (Carrying, thin bar, knots, fuzz balls, warp, weft, stain, line, broken end, hole, netting multiple, thick bar) in yarn-dyed and patterned texture fabric. They divide the network into two parts: segmentation and decision.…”

Section: Machine/deep Learning Approachesmentioning

confidence: 99%

Defect detection in the textile industry using image-based machine learning methods: a brief review

Shahrabadi

Castilla

Guevara

et al. 2022

J. Phys.: Conf. Ser.

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Traditionally, computer vision solutions for detecting elements of interest (e.g., defects) are based on strict context-sensitive implementations to address contained problems with a set of well-defined conditions. On the other hand, several machine learning approaches have proven their generalization capacity, not only to improve classification continuously, but also to learn from new examples, based on a fundamental aspect: the separation of data from the algorithmic setup. The findings regarding backward-propagation and the progresses built upon graphical cards technologies boost the advances in machine learning towards a subfield known as deep learning that is becoming very popular among many industrial areas, due to its even greater robustness and flexibility to map and deal knowledge that is typically handled by humans, with, also, incredible scalability proneness. Fabric defect detection is one of the manual processes that has been progressively automatized resorting to the aforementioned approaches, as it is an essential process for quality control. The goal is manifold: reduce human error, fatigue, ergonomic issues and associated costs, while simultaneously improving the expeditiousness and preciseness of the involved tasks, with a direct impact on profit. Following such research line with a specific focus in the textile industry, this work aims to constitute a brief review of both defect types and Automated Optical Inspection (AOI) mostly based on machine learning techniques, which have been proving their effectiveness in identifying anomalies within the context of textile material analysis. The inclusion of Convolutional Neural Network (CNN) based on known architectures such as AlexNet or Visual Geometry Group (VGG16) on computerized defect analysis allowed to reach accuracies over 98%. A short discussion is also provided along with an analysis of the current state characterizing this field of intervention, as well as some future challenges.

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Section: Machine/deep Learning Approachesmentioning

confidence: 99%

Defect detection in the textile industry using image-based machine learning methods: a brief review

Shahrabadi

Castilla

Guevara

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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%

“…For fabrics of high complexity (i.e., multimodal appearance), supervised approaches that require both normal and anomalous data [6,7] are predominantly used. For example, classification, segmentation and object detection approaches have been successfully adapted to the fabric inspection task [12][13][14][15][16][17]. Moreover, supervised algorithms generally outperform their semi-supervised counterparts [18,19].…”

Section: Introductionmentioning

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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“…Although manual recognition is accurate, it takes a lot of time to train the staff, and the recognition speed on the assembly line is very slow compared to machine recognition [4,5]. With the development of artificial intelligence and computer vision technology, deep learning has potential significance in the application of wood knot defect classification [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

BLNN: Multiscale Feature Fusion‐Based Bilinear Fine‐Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

et al. 2021

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Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

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Fabric Defect Segmentation Method Based on Deep Learning

Cited by 71 publications

References 43 publications

Defect detection in the textile industry using image-based machine learning methods: a brief review

Defect detection in the textile industry using image-based machine learning methods: a brief review

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

BLNN: Multiscale Feature Fusion‐Based Bilinear Fine‐Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

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