Fabric defect detection using saliency of multi‐scale local steering kernel

Zhang, Kaibing; Yan, Yadi; Li, Pengfei; Jing, Junfeng; Wang, Zhen; Xiong, Zenggang

doi:10.1049/iet-ipr.2018.5857

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…The techniques taken for analysis involve Deep CNN [9], CNN [22], DCGAN [21], RDPSO-based EGF [24], Kaibing Zhang et al [19], and YOLOv3 [15] are compared with the proposed CCSO-based DNFN + RideNN.…”

Section: Competing Methodsmentioning

confidence: 99%

“…The CNN can learn automatically using the features of data without including any complex hand-designed features [18]. Deep learning techniques are extensively utilized in the discovery of fabric defects [19]. The process of detecting defects is split into four scenarios that involve classification, segmentation, location, and semantic segmentation of defects.…”

Section: Introductionmentioning

confidence: 99%

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Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Biradar¹,

Sheeparamatti²,

Patil³

2021

Sens Imaging

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The rising rates of costs in labor and growth of computerization in fabric industries made defect detection in fabric a promising domain. For a huge time, manual discovery is extensively utilized in textile industries by trained staff that results in high cost. Meanwhile, the strict quality assessment is done by modern textile industries, which made automatic fabric defect detection a reliable choice. Since defect detection is an important and challenging aspect of modern industrial manufacturing, it is necessary to determine the quality and acceptability of garments and to reduce the cost and time waste caused by defects. Different methods are in practice for effective detection of fabric defects, however, they limit due to many reasons. Thus we proposed a new method named Competitive Cat Swarm Optimizer (CCSO) based Deep neuro-fuzzy network (DNFN) for effective fabric detection. Here, the pre-processing is performed with a median filter for eliminating noise contained in the image. Furthermore, features are extracted that involves Tetrolet transform-based features, statistical features, like energy, entropy, homogeneity, contrast, correlation, and texture feature, like Local gradient pattern. The data augmentation is carried out based on obtained features to make it apposite for processing. The defect detection is carried out using RideNN and DNFN. Here, the training of DNFN is done using the proposed CCSO, which is devised by combining Cat Swarm Optimization and Competitive Swarm Optimizer. The correlation is performed on the outputs of RideNN and DNFN for generating the final output of fabric defect detection. The performance of the proposed CCSO-based DNFN is compared with the different existing methods and the proposed CCSO-based DNFN outperforms with the highest specificity of 0.920, the accuracy of 0.919, and sensitivity of 0.916.

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Section: Competing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Biradar¹,

Sheeparamatti²,

Patil³

2021

Sens Imaging

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“…As the location and state characteristics of the surface defects of ceramic tile are changeable and random, we use the data-driven bottom-up saliency detection method to realize the preliminary detection [27]- [29]. To simplify the computational complexity and improve the detection efficiency, the tile image is transformed from the RGB color space to the hue-saturation-value (HSV) color space.…”

Section: A Defect Saliency Detectionmentioning

confidence: 99%

Detection of Surface Defects in Ceramic Tiles With Complex Texture

Zhang

Peng

et al. 2021

IEEE Access

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To solve the problem of false defect detection owing to the interference of the texture attribute of ceramic tiles, a method for detecting surface defects in complex-textured ceramic tiles is proposed. Based on the visual detection principle of ceramic tile surfaces, an image acquisition system is established to obtain the ceramic tile image. After image segmentation and correction, the surface defects are preliminarily detected using a saliency detection method. Then, the image sub-block containing the defect area is cut out for secondary detection. The false defects are eliminated, and the final detection of the ceramic tile surface defects is completed using the defect determination method. The feasibility and effectiveness of the defect detection method are studied via comparative experiments. Experimental results show that the maximum accuracy rate of the proposed defect detection method is 98.75%, which satisfies the actual detection requirements and confirms the practical significance of the proposed method.INDEX TERMS Complex-textured ceramic tiles, defect determination, image acquisition, image sub-block, saliency detection HUAILIANG ZHANG received his Ph.D.

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“…Multi-scale local texture analysis was performed by calculating the singular value decomposition information of the fabric images converted to the CIE color channel. [20]. In addition, defected regions were determined by measuring the cosine similarity between the defected fabric modeling and the images analyzed with different scales.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Fabric Defect Detection System on Loom Fabric Inspection

Talu

Hanbay

Varjovi

2022

Tekstil Ve Konfeksiyon

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Fabric defect detection is generally performed based on human visual inspection. This method is not effective and it has various difficulties such as eye delusion and labor cost. To deal with these problems, machine learning, and computer vision-based intelligent systems have been developed. In this paper, a novel real-time fabric defect detection system is proposed. The proposed industrial vision system has been operated in real-time on a loom. Firstly, two fabric databases are constructed by using real fabric images and defective patch capture (DPC) algorithm. Thanks to the novel developed fast Fourier transform-based DPC algorithm, defective texture areas become visible and defect-free areas are suppressed, even on complex denim fabric textures. Secondly, an appropriate convolution neural networks (CNN) model integrated negative mining is determined. However, traditional feature extraction and classification approaches are also used to compare classification performances of deep models and traditional models. Experimental results show that our proposed CNN model integrated negative mining can classify the defected images with high accuracy. Also, the proposed CNN model has been tested in real-time on a loom, and it achieves 100% detection accuracy.

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Fabric defect detection using saliency of multi‐scale local steering kernel

Cited by 9 publications

References 34 publications

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Detection of Surface Defects in Ceramic Tiles With Complex Texture

CNN-Based Fabric Defect Detection System on Loom Fabric Inspection

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