An analytical survey of textile fabric defect and shade variation detection system using image processing

Meeradevi, T.; Sasikala, S.; Gomathi, S.; Prabakaran, K.

doi:10.1007/s11042-022-13575-8

Cited by 6 publications

(4 citation statements)

References 77 publications

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“…Many of these approaches are limited in terms of effectiveness for specific defect categories, fabric types, or defect locations within the fabric. Meeradevi et al [142] reviewed six different approaches for fabric defect detection using computer vision: structural, statistical, spectral, learning, hybrid, and others. Among these approaches, the deep learning model achieved the highest accuracy of 99.4%, demonstrating robustness against natural variations in raw data.…”

Section: A Nomenclature and Analysis Of Fabric Defectsmentioning

confidence: 99%

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Guo,

Liu,

et al. 2024

IEEE Access

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Fabric defect detection is a crucial step of quality control in textile enterprises. The use of computer vision inspection technology in the textile industry is key to achieving intelligent manufacturing. This study sought to determine the progress made and future research directions in intelligent fabric surface defect detection by comprehensively reviewing published literature in terms of algorithms, datasets, and detection systems. Initially, the detection methods are classified as traditional and learning-based methods. The traditional methods are subdivided into model, spectral, statistical, and structural approaches. Learningbased methods are categorized into classical machine learning methods and deep learning methods. The principles, model performance, detection rate, real-time performance, and applicability of deep learning methods are highlighted and compared. In addition, the strengths and weaknesses of all the approaches are elaborated. The use of fabric defect datasets and deep learning frameworks is analyzed. Public datasets and commonly used frameworks are collated and organized. The application of existing fabric inspection systems on the market is outlined. Fabric defect types are systematically named and analyzed. Finally, future research directions are discussed to provide guidance for researchers in related fields.

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Section: A Nomenclature and Analysis Of Fabric Defectsmentioning

confidence: 99%

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Guo,

Liu,

et al. 2024

IEEE Access

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“…Because the texture pixels are defined as "a region slowly varying, or approximately periodic" [6], we will build our mathematical model based on this. If we scan a texture through a straight line at any given angle, the one dimensional signal obtained should be:…”

Section: A Frequency Domain Fine Tuningmentioning

confidence: 99%

“…Because of the rapid progress of technology, materials in factories are produced faster and faster. Flat surfaced products such as wood [5], fabric [6] and metal [7] require machine vision systems to detect production defects. Unfortunately, high-speed production of these goods combined with the availability of high-resolution cameras put great strain on the network and the computers that process those images in real time.…”

Section: Introductionmentioning

confidence: 99%

Frequency Domain Improvements to Texture Discrimination Algorithms

Baykal¹

2023

IJACSA

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As the production speeds of factories increase, it becomes more and more challenging to inspect products in real time. The goal of this article is to come up with a computationally efficient texture discrimination algorithm by first testing their ability to localize defects and then increase their efficiency by removing less effective parts of them. Therefore, abilities of the most popular texture classification algorithms such as the GLCM, the LBP and the SDH to localize defects are tested on different datasets. These tests reveal that, on small windows GLCM and SDH perform better. Frequency properties of the textures are used to fine-tune the parameters of these algorithms. Further experiments on three different datasets prove that the accuracy of the algorithms are increased almost twice while decreasing the processing time considerably.

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“…This process depends on human attention and visual capability. These tasks can be more time-consuming and tedious which can result in fatigue and human errors [5]. Hence, conventional models usually obtain an accuracy of 60-75%, despite their extremely slow speed when compared to the rate of production.…”

Section: Introductionmentioning

confidence: 99%

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Sajitha,

Priya

2024

Eng. Technol. Appl. Sci. Res.

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Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

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An analytical survey of textile fabric defect and shade variation detection system using image processing

Cited by 6 publications

References 77 publications

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

Frequency Domain Improvements to Texture Discrimination Algorithms

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

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