Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Pitchaimani, Jeyaraj; Rajan, S. Edward

doi:10.1177/0040517519884124

Cited by 41 publications

(17 citation statements)

References 27 publications

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“…e reported classification accuracy of the proposed model is 97.9%. Jeyaraj et al [81] proposed an innovative method for the detection of defects based on advance deep learning approach. ResNet512-based CNN is used to learn the features from images, and according to Jeyaraj et al, the proposed method also localizes the minute defects.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

“…TILDA textile dataset is used for experiments, and many quantitative values are computed. e classification accuracy is used to compute and to validate the accuracy of the method and results have been compared with some other classifiers also as SVM and Bayesian [81]. Table 10 represents a summary about deep learning-based approaches proposed for fabric defect detection.…”

Section: Deep Learning-based Approachesmentioning

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

106

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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: Deep Learning-based Approachesmentioning

confidence: 99%

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

106

View full text Add to dashboard Cite

show abstract

“…e defective fabric is sold in 45% to 65% of the rst category, and it represents a major loss for any textile industry [1,5]. However, the quality of the fabric can be improved by applying the latest technologies during the manufacturing because customer expectations vary with the quality [6]. erefore, the fabric inspection has a signi cant role in controlling the fabric quality for any textile industry; without controlling the quality and missing the monitoring of the fabric structure, a manufacturer bears the main loss that results in a downfall in the market as well.…”

Section: Introductionmentioning

confidence: 99%

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

Ashraf

Ijaz

Asif

et al. 2022

Mathematical Problems in Engineering

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Convolution neural network (CNN) is one of the most popular machine learning techniques that is being used in many applications like image classification, image analysis, textile archives, object recognition, and many more. In the textile industry, the classification of defective and nondefective fabric is an essential and necessary step to control the quality of fabric. Traditionally, a user physically inspects and classifies the fabric, which is an ineffective and tedious activity. Therefore, it is desirable to have an automated system for detecting defects in the fabric. To address these issues, this research proposes a solution for classifying defective and nondefective fabric using deep learning-based framework. Therefore, in this research, an image processing technique with CNN-based GoogleNet is presented to classify defective and nondefective fabric. To achieve the purpose, the system is trained using different kinds of fabric defects. The performance of the proposed approach was evaluated on the textile texture TILDA dataset, and achieved a classification accuracy of 94.46%. The classification results show that the proposed approach for classifying defective and nondefective fabric is better as compared to other state-of-the-art approaches such as Bayesian, BPNN, and SVM.

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“…Chua et al [ 5 ] reviewed current process monitoring/control systems for metal AM and proposed a comprehensive real-time inspection method and a closed-loop monitoring system to improve the quality of AM printed parts. To ensure the internal quality while improving the efficiency of AM product inspection, deep learning is commonly applied in both industry and academia [ 6 , 7 , 8 ]. Typically, the deep learning-based defects inspection involves three steps: image acquisition, image classification, and defects localization.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Acceleration on Additive Manufacturing Defects Inspection

Luo

Chen

2021

Sensors

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Additive manufacturing (AM) has gained increasing attention over the past years due to its fast prototype, easier modification, and possibility for complex internal texture devices when compared to traditional manufacture processing. However, potential internal defects are occurring during AM processes, and it requires real-time inspections to minimize the costs by either aborting the processing or repairing the defect. In order to perform the defects inspection, first the defects database NEU-DET is used for training. Then, a convolution neural network (CNN) is applied to perform defects classification. For real-time purposes, Field Programmable Gate Arrays (FPGAs) are utilized for acceleration. A binarized neural network (BNN) is proposed to best fit the FPGA bit operations. Finally, for the image labeled with defects, the selective search and non-maximum algorithms are implemented to help locate the coordinates of defects. Experiments show that the BNN model on NEU-DET can achieve 97.9% accuracy in identifying whether the image is defective or defect-free. As for the image classification speed, the FPGA-based BNN module can process one image within 0.5 s. The BNN design is modularized and can be duplicated in parallel to fully utilize logic gates and memory resources in FPGAs. It is clear that the proposed FPGA-based BNN can perform real-time defects inspection with high accuracy and it can easily scale up to larger FPGA implementations.

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Effective textile quality processing and an accurate inspection system using the advanced deep learning technique

Cited by 41 publications

References 27 publications

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

FPGA-Based Acceleration on Additive Manufacturing Defects Inspection

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