Automatic fabric defect detection with a wide-and-compact network

Li, Yuyuan; Zhang, Dong; Lee, Dah-Jye

doi:10.1016/j.neucom.2018.10.070

Cited by 94 publications

(43 citation statements)

References 19 publications

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“…Jing et al [17] proposed an improved method of fabric defect classification based on the AlexNet network, which achieved the defect classification of yarn-dyed fabric. A compact network is proposed for the defect classification of knitted fabrics, which performs well in detection accuracy with a smaller model size [18]. A YOLO model-based fabric defect location method is proposed to improve the speed of defect detection.…”

Section: Related Workmentioning

confidence: 99%

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

Wang

Jing

2020

IEEE Access

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Surface inspection is a necessary process of fabric quality control. However, it remains a challenging task owing to diverse types of defects, various patterns of fabric texture, and application requirements for detection speed. In this paper, a lightweight deep learning model is therefore proposed to complete the segmentation of fabric defects. The input of the model is a fabric image, and the output is a binary image. Generally known, a deep learning model usually needs much data to update the parameters. Still, as an abnormal phenomenon, fabric defects are unpredictable, which makes it impossible to collect a large number of data. Distinct from other models, the proposed method is a supervised network but does not need manually labeled samples for training. A fake sample generator is designed to simulate the defect image, which only needs the defect-free fabric image. The proposed model is trained with fake samples and verified with real samples. The experimental results show that the model trained with false data is useful and achieves high segmentation accuracy on real fabric samples. Besides, a loss function is proposed to deal with the problem of imbalance between the number of background pixels and the number of defective pixels in the fabric image. Comprehensive experiments were performed on representative fabric samples to verify the segmentation accuracy and detection speed of this method. INDEX TERMS Fabric defect; deep learning; image segmentation; defect detection; imbalanced dataset.

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Section: Related Workmentioning

confidence: 99%

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

Wang

Jing

2020

IEEE Access

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“…Recent research on defect detection mainly includes dictionary learning based methods [23][24], sparse plus low rank strategy based methods [25][26] and convolutional neural network (CNN) based methods [27]. Dictionary learning based methods learned a dictionary from many image patches, where the image patches are acquired by defect-free images.…”

Section: Related Workmentioning

confidence: 99%

A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection

Jiang¹,

Jin²,

Wang³

et al. 2020

KSII TIIS

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In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

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“…Supervised learning is the most widely used paradigm, often using deep convolutional neural networks (CNNs) for defect detection. Supervised learning methods based on CNN can achieve high defect detection accuracy given a large number of training data (Kim et al [19], Park et al [20], Li et al [21], Nakazawa et al [22], Jeyaraj et al [23], Liu et al [24], Saiz et al [25], Zhang et al [26], Soukup et al [27]). However, the disadvantage is that they rely heavily on manpower to collect and label training samples.…”

Section: Introductionmentioning

confidence: 99%

A Generic Semi-Supervised Deep Learning-Based Approach for Automated Surface Inspection

et al. 2020

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Automated surface inspection (ASI) is critical to quality control in industrial manufacturing processes. Recent advances in deep learning have produced new ASI methods that automatically learn highlevel features from training samples while being robust to changes and capable of detecting different types of surfaces and defects. However, they usually rely heavily on manpower to collect and label training samples. In this paper, a generic semi-supervised deep learning-based approach for ASI that requires a small quantity of labeled training data is proposed. While the approach follows the MixMatch rules to conduct sophisticated data augmentation, we introduce a new loss function calculation method and propose a new convolutional neural network based on a residual structure to achieve accurate defect detection. An experiment on two public datasets (DAGM and NEU) and one industrial dataset (CCL) is carried out. For public datasets, the experimental results are compared against several best benchmarks in the literature. For the industrial dataset, the results are compared against deep learning methods based on benchmark neural networks. The proposed method achieves the best performance in all comparisons. In addition, a comparative experiment of model performance given a different number of labeled samples is conducted, demonstrating that the proposed method can achieve good performance with few labeled training samples.

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Automatic fabric defect detection with a wide-and-compact network

Cited by 94 publications

References 19 publications

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

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

A Sobel Operator Combined with Patch Statistics Algorithm for Fabric Defect Detection

A Generic Semi-Supervised Deep Learning-Based Approach for Automated Surface Inspection

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