Fabric Defect Detection in Textile Images Using Gabor Filter

Sajid, Tanveer

doi:10.9790/1676-0323338

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Mathematical algorithms learn from and analyze data to generate predictions and judgements in machine learning. Many other spectrum approaches exist, but some of the most well-known are the Fourier transform, Gabor transform, wavelet transform, and discrete cosine transform [18][19][20]. Table 2 details the algorithms discussed in this survey.…”

Section: Fabric Defect Detection Methodsmentioning

confidence: 99%

Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics

Ms Shital K Dhamal, Dr Chandani Joshi, Dr D.S Chouhan

2023

tjjpt

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Regarding maintaining product quality and satisfying customer expectations, fabric defect analysis is vital in textile manufacturing. This research provides a comprehensive assessment of many techniques used for analyzing and pinpointing flaws in standard and Jacquard textiles. This study considers machine vision systems and AI-based algorithms as two examples of cutting-edge automation alongside more conventional human inspection procedures. Manual inspection techniques are extensively reviewed in the first section, with particular emphasis on these techniques' inherent subjectivity and resource inefficiency. More objective and effective defect detection techniques are highlighted as a solution to the problems caused by human bias. The research then looks into automated methods, examining how recent developments in image processing, computer vision, and pattern recognition have the potential to greatly improve the accuracy and speed with which defects may be detected. The major emphasis of this investigation is the use of AI, namely machine learning and deep learning models, for fabric defect identification. This demonstrates how AI may revolutionize textile manufacturing by automating flaw identification and categorization processes. Accuracy, efficiency, scalability, cost-effectiveness, and adaptation to varied fabric compositions, such as basic and complicated Jacquard textiles, are only some aspects considered throughout the evaluation. In addition, the research addresses the problems and opportunities in the field of fabric defect analysis right now. The paper presents prospective improvements, such as hybrid methods and real-time monitoring systems, to solve current constraints and pave the way for a more robust defect analysis framework. These innovations aim to contribute to sustainable practices and customer happiness in the textile manufacturing industry by fostering effective quality monitoring and production optimization. In conclusion, this study provides a thorough comprehension of fabric defect analysis procedures, which is helpful for professionals and academics in the field. The results fuel the never-ending development of quality assurance techniques, resulting in improvements that raise standards, shorten production times, and give the textile business a fighting chance in the market.

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Section: Fabric Defect Detection Methodsmentioning

confidence: 99%

Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics

Ms Shital K Dhamal, Dr Chandani Joshi, Dr D.S Chouhan

2023

tjjpt

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“…In the specific area of defect detection, the industry employs a comprehensive quality-control system that is both proactive and reactive. Advanced technologies, such as high-resolution imaging and machine learning algorithms, are increasingly utilized to identify imperfections that could compromise the quality of the fabric (Sajid, 2012;Tsang et al, 2016). These systems can detect a wide array of defects, ranging from color inconsistency and pattern misalignment to structural weaknesses in the fabric .…”

Section: Headingmentioning

confidence: 99%

“…Integrating IoT sensors into manufacturing settings is paving the way for "smart manufacturing." IoT sensors are being deployed across a multitude of industries to enable real-time monitoring, data-driven insights, and optimization opportunities (Sajid, 2012). In automobile manufacturing, IoT sensors are integrated throughout the assembly line, tracking component quality, detecting potential defects, and streamlining maintenance processes (Yapi et al, 2015).…”

Section: Iot and Smart Manufacturingmentioning

confidence: 99%

Enhancing Textile Quality Control With Iot Sensors: A Case Study of Automated Defect Detection

2024

GMJ

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The traditional approach to textile quality control, predominantly reliant on manual inspection, is fraught with precision, speed, and reliability challenges. This case study explores the deployment of an Internet of Things (IoT) based system, incorporating sophisticated image processing and machine learning techniques, aimed at automating fabric defect detection in a mid-sized textile manufacturing setting. The study reveals a notable enhancement in the accuracy of defect detection and considerable improvements in inspection speed and operational efficiency. Implementing this IoT system resulted in a marked reduction in manual labor requirements and provided a compelling cost-benefit ratio, underscoring the system's financial viability. Furthermore, the case study details significant operational benefits, such as a 94.25% accuracy in defect detection and a reduction in inspection time from 10.78 to 2.47 minutes per unit. These outcomes affirm the transformative potential of IoT technologies in refining textile quality control processes, advocating for a shift towards more sustainable, quality-focused, and efficient manufacturing paradigms.

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“…The major contributions of this work are: a novel application of LT for defect detection in industrial flat surface products, and a fast and efficient context based algorithm for defect detection compared with the stateof-the-art approaches such as log-Gabor (LG) filters, 3 multiscale multidirectional autocorrelation, 4 Gabor wavelets, [5][6][7][8][9][10][11][12] wavelet transform-based techniques, 13 and filter-based approaches. 14,15 The implemented defect detection approach achieves better results compared with those achieved by the state-of-the-art approaches such as LG wavelets but with lower computational complexity resulting in a 6-10 times faster defect detection system.…”

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confidence: 99%

Homoscedasticity for defect detection in homogeneous flat surface products

Raafat

Tolba

2014

Textile Research Journal

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Homoscedasticity of homogeneous flat surface products is a necessary condition for a high quality product. The quality of homogeneous flat surface products, like paper sheets, steel slabs, textiles, and glasses, plays a crucial role in raising the profile of the manufacturing companies. This paper presents a new approach for defect detection using the Levene's test, which is used for testing the homogeneity of variances of samples drawn from the same population. It is assumed that the variances of samples taken from the same population are equal. Occurrence of defects results in a Levene's test measure that is higher than some critical value indicating that the null hypothesis of equal variances is rejected. Noise immunity of the proposed technique is ensured through pre-filtering the fabric image using the Wiener filter that is an edge preserving filter. The robustness to variations of the sliding window size and the structures of fabric is analyzed. The major advantages of the proposed technique are the low computational complexity and noise immunity while maintaining high accuracy.

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Fabric Defect Detection in Textile Images Using Gabor Filter

Cited by 6 publications

References 9 publications

Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics

Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics

Enhancing Textile Quality Control With Iot Sensors: A Case Study of Automated Defect Detection

Homoscedasticity for defect detection in homogeneous flat surface products

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