Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

Jing, Junfeng; Fan, Xiaoting; Li, Pengfei

doi:10.14257/ijmue.2016.11.6.09

Cited by 22 publications

(7 citation statements)

References 20 publications

(32 reference statements)

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“…Jing et al [53] proposed a new technique for real-time automated defect detection in textile cloth. e proposed research is based on the multiple Gabor filters (MGFs), which are used for feature extraction, and kernel principal component analysis (KPCA) is used for the reduction of high-dimension data to identify many uniform and structural detects.…”

Section: Feature Fusion-based Defect Detection Techniquesmentioning

confidence: 99%

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Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

113

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There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

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Section: Feature Fusion-based Defect Detection Techniquesmentioning

confidence: 99%

“…Proposed two methods to detect seam using images, textural analysis by wavelet energy method and by features extraction in CIELAB color space in the image Jing et al [53] Defect-free and defective (fabric) images which have been taken from the TILDA textile texture database have been used…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

113

View full text Add to dashboard Cite

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“…In recent years, a large number of scholars have proposed many fabric defect detection algorithms to reduce the enterprises loss caused by fabric defects [12]. Jing and Bissi et al [13]- [16] used a Gabor filter to filter defect textures and extract texture features, then used the golden image subtraction or Principal Component Analysis_(PCA) algorithm to segment defects from normal textures. The defect detection of raw fabric obtains good results, but the universality of the algorithm needs to be further improved.…”

Section: Related Workmentioning

confidence: 99%

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

Kang

Zhang

2020

IEEE Access

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Due to the complex diversity of both fabric texture and defect, fabric defect detection is a challenge topic. However, most of existing defect detection methods can still detect only one type of fabric defects. In order to solve this problem, we propose a universal and adaptive defect detection algorithm based on dictionary learning for detecting various defects of different fabric texture. Firstly, in order to make the image more balanced and improve the detection accuracy, according to the complexity of fabric texture and the degree of brightness and darkness of the background, we segment defect-free image according to a certain size to obtain the image joint matrix. In order to make the algorithm more universal, next we form a random dictionary instead of over-complete dictionary and fixed dictionary by randomly select feature columns from the image joint matrix, which can extract the most important texture and background information of fabric. Then, we use the Orthogonal Matching Pursuit algorithm to obtain the sparse representation of defect-free image. On this basis, we use the K-Singular Value Decomposition algorithm to update the dictionary iteratively to get the final dictionary. Afterward, the defective image is reconstructed with the learned dictionary to obtain the reconstruction error. Finally, the correction coefficient is obtained through reconstruction error analysis, then the corresponding adaptive threshold is used to detect and mark the location of the defects. The performance of the algorithm is evaluated on the fabric database of raw fabric, yarn-dyed fabric and patterned fabric. The experimental results show that the proposed algorithm has good universality, adaptability and a high detection success rate for different types of fabrics. Particularly, the average detection success rate of dark-red fabric and dot-patterned fabric can reach 100%, and the Precision and the Recall of defect detection evaluation index can also reach 100%.

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“…Perpaduan metode Gabor Filter dan KPCA telah digunakan untuk pengurangan dimensi nonlinier untuk mendeteksi cacat kain yang lebih akurat dan pada akhirnya jenis jenis benang cacat telah ditemukan. Hasil eksperimen itu menunjukan nilai akurasi kecacatan kain sampai 96% presentase ini cukup memuaskan dan menjanjkain namun diharapkan agar terus dikembangkan dimasa yang akan datang [13].…”

Section: Pendahuluanunclassified

Implementasi Metode Ekstraksi Fitur Gabor Filter dan Probablity Neural Network (PNN) untuk Identifikasi Kain Tapis Lampung

et al. 2020

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Tapis Fabric is a traditional clothing of the people of Lampung in the form of a shawl cloth or a sarong made of woven cotton thread with various motifs and ornaments, silver thread or gold thread by embroidered or punched. The pattern of filter cloth is quite complex, unlike the pattern of fabric in general, with its own uniqueness that has become the culture of Lampung society until now. This filter cloth will be investigated by identifying the three types of filter cloth, namely Sasab, Bintang Perak and Gunung Beradu and see the results of its identification. The method used to identify is by combining the Gabor Filter feature extraction method which has frequency and orientation parameters and Probability Neural Network classification methods. Previously, the combination of these two methods was used to identify objects with simple patterns. The results are quite good, such as detecting faces, leaf patterns, and other simple patterns. This research is expected to get maximum identification results on the filter cloth even though it has a pattern that is not simple and will be used as a research report to determine the suitability of the method used for the filter object.

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Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

Abstract: Abstract

Cited by 22 publications

References 20 publications

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

Implementasi Metode Ekstraksi Fitur Gabor Filter dan Probablity Neural Network (PNN) untuk Identifikasi Kain Tapis Lampung

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