Fabric defect detection using deep learning

Şeker, Abdulkadir; Peker, Kadir A.; Yüksek, Ahmet Gürkan; Delibaş, Emre

doi:10.1109/siu.2016.7496020

Cited by 29 publications

(20 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep NNs (DNNs) are currently popular in defect detection, for example, in Ref. [40], the authors developed a method based on deep learning to fabric defect detection. Convolution NNs (CNNs) have attracted much attention in many fields such as object detection; in Ref.…”

Section: Detection Methodsmentioning

confidence: 99%

A Public Fabric Database for Defect Detection Methods and Results

Silvestre-Blanes

Albero-Albero

Miralles

et al. 2019

Autex Research Journal

View full text Add to dashboard Cite

The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.

show abstract

Section: Detection Methodsmentioning

confidence: 99%

A Public Fabric Database for Defect Detection Methods and Results

Silvestre-Blanes

Albero-Albero

Miralles

et al. 2019

Autex Research Journal

View full text Add to dashboard Cite

show abstract

“…Şeker et al 9 have presented a paper on fabric defect detection that uses deep leaning techniques. A methodology is developed on the defect detection to obtain high precision with image processing techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Defective and nondefective classif ication of fabric images using shallow and deep networks

Elemmi¹,

Anami

Malvade³

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

The defect detection is an important activity in quality analysis and control in the fabric industry. The presented work gives a comparative analysis of artificial neural network and deep learning architectures. The MobileNet and deep residual network (ResNet) are deployed to classify the defective and nondefective fabric images. The hand-crafted morphological features are used in fabric image analysis along with feed backward selection feature reduction method to obtain the significant features. The overall classification rates of 95.3%, 98.2%, and 99.65% are obtained for Shallow, ResNet, and MobileNet architectures, respectively. The MobileNet model has given a maximum classification rate than Shallow and ResNet architectures. The work finds applications in apparel industry, quality analysis, cost estimation, online purchase of fabric, Industry 4.0, and so on.

show abstract

“…Recent advances in deep learning technology (e.g., CNN) have achieved human-level classification performance [8], and provided advanced analytical tools for analyzing big data from manufacturing [9]. Deep learning technologies, such as surface defect classification of steel sheets [12] and fabric defect classification [13], have been introduced in the manufacturing sector and automatic inspection techniques have been widely applied in manufacturing processes to ensure the high quality and performance of products [14]. The semiconductor industry has also shown interest in deep learning applications: Nakazawa and Kulkarni applied a CNN for wafer-map classification [15] A drawback of these methods is that they require more than several thousand training data points with accurate groundtruth labels.…”

Section: Related Workmentioning

confidence: 99%

A CNN-Based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing

Imoto

Nakai

Ike

et al. 2019

IEEE Trans. Semicond. Manufact.

View full text Add to dashboard Cite

In this paper, we focus on a defect analysis task that requires engineers to identify the causes of yield reduction from defect classification results. We organize the analysis work into three phases: defect classification, defect trend monitoring and detailed classification. To support the first and third engineer's analytical work, we use a convolutional neural network based on the transfer learning method for automatic defect classification. We evaluated our proposed methods on real semiconductor fabrication data sets by performing a defect classification task using a scanning electron microscope image and thoroughly examining its performance. We concluded that the proposed method can classify defect images with high accuracy while lowering labor costs equivalent to one-third the labor required for manual inspection work.

show abstract

Fabric defect detection using deep learning

Cited by 29 publications

References 2 publications

A Public Fabric Database for Defect Detection Methods and Results

A Public Fabric Database for Defect Detection Methods and Results

Defective and nondefective classif ication of fabric images using shallow and deep networks

A CNN-Based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing

Contact Info

Product

Resources

About