Fabric defect inspection based on lattice segmentation and Gabor filtering

Jia, Liang; Chen, Chen; Liang, Jiuzhen; Hou, Zhenjie

doi:10.1016/j.neucom.2017.01.039

Cited by 96 publications

(63 citation statements)

References 51 publications

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“…Spectral approaches treat the solution of differential equation as the synthesis of some basis functions. There are several spectral based detection method: Gabor transform [10][11][12][13], wavelet transform [14][15][16][17], fourier transform [18][19][20]. Gabor filters analyse textures in both frequency and spatial domain.…”

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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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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show abstract

“…In order to illustrate the effectiveness of Fast-DDF, we compared the current mainstream fabric defect detection methods. With some widely used defect detection methods such as Gabor filter, LBP, lattice segmentation assisted by Gabor filters (LSG) [11], GLCM [4] and Faster RCNN [24]. All of the above experiments were performed on Matlab, and the parameters of above all methods are all default values.…”

Section: Frame Experiments Of Defect Extractionmentioning

confidence: 99%

“…Spectral methods include Fourier Manuscript transform [9], Gabor filter [10], [11] and wavelet transform [12]. Model methods include autoregressive models and Markov fields [13].…”

Section: Introductionmentioning

confidence: 99%

A Fast Fabric Defect Detection Framework for Multi-Layer Convolutional Neural Network Based on Histogram Back-Projection

Sun

Zhou

Gao

et al. 2019

IEICE Trans. Inf. & Syst.

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In this paper we design a fast fabric defect detection framework (Fast-DDF) based on gray histogram back-projection, which adopts end to end multi-convoluted network model to realize defect classification. First, the back-projection image is established through the gray histogram on fabric image, and the closing operation and adaptive threshold segmentation method are performed to screen the impurity information and extract the defect regions. Then, the defect images segmented by the Fast-DDF are marked and normalized into the multi-layer convolutional neural network for training. Finally, in order to solve the problem of difficult adjustment of network model parameters and long training time, some strategies such as batch normalization of samples and network fine tuning are proposed. The experimental results on the TILDA database show that our method can deal with various defect types of textile fabrics. The average detection accuracy with a higher rate of 96.12% in the database of five different defects, and the single image detection speed only needs 0.72s. key words: back-projection, gray histogram, fabric detection, multi-layer convolutional neural network Guodong Sun is a professor in the School of Mechanical Engineering at the Hubei University of Technology. He received his BS degree in energy and power engineering and his PhD degree in mechanical and electronic engineering from Huazhong University of Science and Technology in 2002 and 2008, respectively. His current research interests include machine vision and imaging processing. Zhen Zhoureceived the B.S. degree from in 2017. He is currently pursuing the M.S. degree with the School of Mechanical Engineering, Hubei University of Technology, Wuhan, China. His current research interests are machine learning and imaging processing.

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“…Surface defect inspection plays a vital role in manufacturing processes for product quality control. It is used throughout many different domains, such as metal [1][2][3], concrete [4], plastic [5], or fabric [6,7] surface anomaly detection. However, these tasks are often carried out manually, requiring specialized skills to be mastered, which is quite inefficient and limits the rapid development of relevant industrial applications.…”

Section: Introductionmentioning

confidence: 99%

A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies

Liu

Yang

Guo

et al. 2020

Sensors

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We explore a one-stage method for surface anomaly detection in industrial scenarios. On one side, encoder-decoder segmentation network is constructed to capture small targets as much as possible, and then dual background suppression mechanisms are designed to reduce noise patterns in coarse and fine manners. On the other hand, a classification module without learning parameters is built to reduce information loss in small targets due to the inexistence of successive down-sampling processes. Experimental results demonstrate that our one-stage detector achieves state-of-the-art performance in terms of precision, recall and f-score.

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Fabric defect inspection based on lattice segmentation and Gabor filtering

Cited by 96 publications

References 51 publications

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

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

A Fast Fabric Defect Detection Framework for Multi-Layer Convolutional Neural Network Based on Histogram Back-Projection

A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies

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