Fabric defect detection using a hybrid particle swarm optimization-gravitational search algorithm and a Gabor filter

So, Yongguk; Kim, Jongchol; Hwang, Hyok

doi:10.1364/josaa.391317

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Selection of parameters based on Gabor. The extraction of texture feature in frequency domain is based on Gabor wavelet transform, [12][13][14] which is making the convolution operation of Gabor filter and original image. Then the converted fiber image is extracted texture feature values by adopting the Gray-Scale difference statistics method.…”

Section: Improved Algorithmmentioning

confidence: 99%

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Zhu

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

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Section: Improved Algorithmmentioning

confidence: 99%

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Zhu

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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“…The fabric defect detection deep learning network model adopts the focal loss function 21 : where g ( X ) is the probability that the YOLO network model predicts the correct type of defect, αt is the correction coefficient, and γ is a hyperparameter.…”

Section: Fabric Defect Detection Algorithmmentioning

confidence: 99%

An intelligent defect detection system for warp-knitted fabric

Gangsheng

Yang

et al. 2021

Textile Research Journal

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In textile factories, the most typical warp-knitted fabric defects include point defects, holes, and color differences. Traditional manual inspection methods are inefficient for detecting these defects. Existing intelligent inspection systems often have a single function. Factories require a real-time inspection system that can detect common defects and color difference. The YOLO (you only look once) neural network is faster than the two-stage neural network and has lower hardware requirements. The system’s color difference detection algorithm compares the color difference between the standard image and the image to be measured and records where the color difference value is exceeded. Finally, the comparison of the factory application proves that the designed system has good real-time performance and accuracy and can meet the fabric inspection requirements of warp-knitted fabric factories.

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“…However, challenges exist in PSO-based approaches. The fitness function plays a key role in defect detection, requiring the ability to differentiate between defective and nondefective areas and identify various types of defects [ 24 ]. For instance, in a study on leather defect detection, a modified fitness function using selective-band Shannon entropy improved segmentation efficiency [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

Shang

Chen

et al. 2023

Sensors

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This paper presents an advanced methodology for defect prediction in radiographic images, predicated on a refined particle swarm optimization (PSO) algorithm with an emphasis on fluctuation sensitivity. Conventional PSO models with stable velocity are often beleaguered with challenges in precisely pinpointing defect regions in radiographic images, attributable to the lack of a defect-centric approach and the propensity for premature convergence. The proposed fluctuation-sensitive particle swarm optimization (FS-PSO) model, distinguished by an approximate 40% increase in particle entrapment within defect areas and an expedited convergence rate, necessitates a maximal additional time consumption of only 2.28%. The model, also characterized by reduced chaotic swarm movement, enhances efficiency through the modulation of movement intensity concomitant with the escalation in swarm size. The FS-PSO algorithm’s performance was rigorously evaluated via a series of simulations and practical blade experiments. The empirical findings evince that the FS-PSO model substantially outperforms the conventional stable velocity model, particularly in terms of shape retention in defect extraction.

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Fabric defect detection using a hybrid particle swarm optimization-gravitational search algorithm and a Gabor filter

Cited by 8 publications

References 17 publications

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Identification method of cashmere and wool based on texture features of GLCM and Gabor

An intelligent defect detection system for warp-knitted fabric

Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

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