Automatic Thresholding Method Developed With Entropy For Fabric Defect Detection

Üzen, Hüseyin; FIRAT, Hüseyin; Karcı, Ali; Hanbay, Davut

doi:10.1109/idap.2019.8875890

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Son zamanlarda, yüzey hata tespiti için derin öğrenme tabanlı Evrişimsel Sinir Ağları (ESA) kullanılarak yüksek performanslar elde edilmiştir [6], [9]. Evrişim, havuzlama ve doğrusal olmayan aktivasyon katmanlarından oluşan ESA, hata tespiti için etkili ve güçlü öznitelikleri otomatik bir şekilde elde etmektedir [1], [10]- [12]. ESA tabanlı yüzey hata tespit yöntemleri, Görüntü-seviyesinde, Bölgeseviyesinde ve Piksel-seviyesinde olmak üzere 3 yöntemden oluşmaktadır [1], [4].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Yüzey Hata Tespiti için Sonuç Ağırlıklandırma Tabanlı Resnet Öznitelik Piramit Ağ Mimarisi

Üzen

Türkoğlu

Hanbay

2021

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Theory and Methods: In this study, Result Weighting-based Resnet Feature Pyramid Network (SA-ROPA) architecture was developed for surface defect detection. The proposed model, pretrained Resnet50 network architecture based on a transfer learning approach was used to deal with the problem of insufficient samples. In addition, a new approach called Result Weighting (SA), instead of directly combining features from different levels of the FPN architecture, was used. In this approach, four different defect detection results were obtained simultaneously using different levels of feature from the Resnet50 network. Then, the different detection results were combined in the depth dimension and the point convolution process was applied to obtain the final result prediction.Results: MT, MVTec-Texture and AITEX surface defect detection datasets were used to test the proposed SA-ROPA architecture. In experimental studies, the mIoU value obtained for the MT, MVTec-Texture and AITEX datasets using the proposed model was calculated as 79.92%, 76.37%, and 82.72%, respectively. As a result, the proposed model was achieved higher success than the state-of-the-art methods and previous studies. Conclusion:In the experimental results, it was observed that the surface defects were detected with high performance using the proposed SA-ROPA model. These results showed that the proposed model can be used for automatic surface defect detection in manufacturing systems. In addition, it was envisaged that this study in which extensive experimental studies were carried out, will lead to further work in the field of automatic surface defect detection.

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Section: Gi̇ri̇ş (Introduction)unclassified

Yüzey Hata Tespiti için Sonuç Ağırlıklandırma Tabanlı Resnet Öznitelik Piramit Ağ Mimarisi

Üzen

Türkoğlu

Hanbay

2021

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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“…In recent years, deep learning-based techniques have made great advances in many computer vision tasks, such as object detection [9], natural language processing [10], semantic segmentation [11][12], and image classification [13][14], because they can automatically extract robust and distinctive features from the original data in a hierarchical manner. Progress in deep learning techniques takes HSIC to a novel level.…”

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confidence: 99%

Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification

FIRAT

Asker

Hanbay

2022

Balkan Journal of Electrical and Computer Engineering

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Convolutional neural networks (CNNs) are one of deep learning methods that are often used to solve the problem of hyperspectral image classification (HSIC). CNN has a strong feature learning ability that can ensure more distinctive features for higher quality HSIC. The traditional CNN-based methods mainly use the 2D CNN for HSIC. However, with 2D CNN, only spatial features are extracted in HSI. Good feature maps cannot be extracted from spectral dimensions with the use of 2D CNN alone. By using 3D CNN, spatial-spectral features are extracted simultaneously. However, 3D CNN is computationally complex. In this study, a hybrid CNN method, which is a combination of 3D CNN and 2D CNN, is improved to solve the two problems described above. Using hybrid CNN decreases the complexity of the method compared to using only 3D CNN and can perform well against a limited number of training samples. On the other hand, in Hybrid CNN, depthwise separable convolution (DSC) is used, which decreases computational cost, prevents overfitting and enables more spatial feature extraction. By adding DSC to the developed hybrid CNN, a hybrid depthwise separable convolutional neural network is obtained. Extensive applications on frequently used HSI benchmark datasets show that the classification performance of the proposed network is better than compared methods.

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Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

et al. 2022

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Automatic Thresholding Method Developed With Entropy For Fabric Defect Detection

Cited by 7 publications

References 8 publications

Yüzey Hata Tespiti için Sonuç Ağırlıklandırma Tabanlı Resnet Öznitelik Piramit Ağ Mimarisi

Yüzey Hata Tespiti için Sonuç Ağırlıklandırma Tabanlı Resnet Öznitelik Piramit Ağ Mimarisi

Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification

Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

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