Classification of Defective Fabrics Using Capsule Networks

Kahraman, Yavuz; Durmuşoğlu, Alptekin

doi:10.3390/app12105285

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…Additionally, a segmentation network with a decision network was proposed by Huang et al [32], with the reduced number of images needed to achieve accurate segmentation results being a major advantage. Furthermore, a deep learning model to classify fabric defects in seven categories based on CapsNet was proposed by Kahraman et al [33], achieving an accuracy of 98.71%.…”

Section: Related Workmentioning

confidence: 99%

Using Object Detection Technology to Identify Defects in Clothing for Blind People

Rocha

Pinto

Machado

et al. 2023

Sensors

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Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community.

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Section: Related Workmentioning

confidence: 99%

Using Object Detection Technology to Identify Defects in Clothing for Blind People

Rocha

Pinto

Machado

et al. 2023

Sensors

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“…The success rate was 94.65%. In another study by these authors, Kahraman and Durmusoglu 65 proposed capsule networks, a deep learning method based on CNN, to classify fabric defects. The TILDA dataset as source data for training and testing phases was employed.…”

Section: Deep Learning-based Fabric Defect Detectionmentioning

confidence: 99%

“…Investigated papers were analyzed and summarized in terms of method, dataset, classification or number of classes, performance as success, and comparison. 15,24,44–49,52–77…”

Section: Deep Learning-based Fabric Defect Detectionmentioning

confidence: 99%

Deep learning-based fabric defect detection: A review

Kahraman

Durmuşoğlu

2022

Textile Research Journal

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The use of the deep learning approach in the textile industry for the purpose of defect detection has become an increasing trend in the past 20 years. The majority of publications have investigated a specific problem in this field. Furthermore, many of published reviews or survey articles preferred to investigate papers from a more general perspective. Compared with published review publications, this study is the first up-to-date study that investigates the implementation of deep learning approaches for the detection of fabric defects from 2003 to the present. As the main objective of this study is to review deep learning-based fabric defect detection, the publications regarding fabric defect detection by using deep learning are examined. The methods, database, performance rates, comparisons, and architecture type of these works were compared with each other. The most widely used deep learning architectures customized deep convolutional neural networks, long short-term memory, generative adversarial networks, and autoencoders. Besides the use of the most used deep learning algorithms, the advantages and disadvantages of these approaches have also been expressed.

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“…Dlamini et al [24] proposed an improved YOLOv4 network: first, preprocess the flawed image to decompose the image into smaller sizes, and then use the filtering method to denoise the flawed features to enhance the robustness of the model, then deploy the trained model to the hardware; the accuracy of detecting specific flaws reached 95.3%. Kahraman et al [25] proposed a capsule Network, unlike the traditional CNN, which causes information loss, the capsules are capable of holding more information and are a group of neurons that include not only the probability of a particular object's presence, but also different informative values related to instantiation parameters. The result shows the CapsNet achieved a performance value of 98.7%.…”

Section: Introductionmentioning

confidence: 99%

Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO

Yang

Wang

et al. 2022

Applied Sciences

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Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the field of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classification and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect.

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Classification of Defective Fabrics Using Capsule Networks

Cited by 11 publications

References 34 publications

Using Object Detection Technology to Identify Defects in Clothing for Blind People

Using Object Detection Technology to Identify Defects in Clothing for Blind People

Deep learning-based fabric defect detection: A review

Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO

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