Texture defect classification with multiple pooling and filter ensemble based on deep neural network. (1st August 2021)

• Record Type: Journal Article
• Authors: Uzen, Huseyin; Turkoglu, Muammer; Hanbay, Davut
• Is Part Of: Expert systems with applications. Volume 175(2021)
• Publication Date: 2021-08-01
• Subjects: Texture defect recognition -- Deep features -- Support vector machine -- Data augment -- Classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114838
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The paper presents a novel Multiple Pooling and Filter approach based on a Deep Neural Network. The pre-trained deep network was used to extract features from texture images. A consistent and precise detection model is assured for the classification of texture defects. The proposed model has high accuracy at or above 95 percent. Abstract: Fabric quality control is one of the most important phases of production in order to ensure high-quality standards in the fabric production sector. For this reason, the development of successful automatic quality control systems has been a very important research subject. In this study, we propose a Multiple Pooling and Filter approach based on a Deep Neural Network (MPF-DNN) for the classification of texture defects. This model consists of three basic stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the texture images were first divided into n × n equal parts. Then, median filtering and pooling processes were applied to each piece prior to performing image merging. In the proposed pre-treatment stage, it is aimed to clarify fabric errors and increase performance. For the feature extraction stage, deep features were extracted from the texture images using the pretrained ResNet101 model based on the transfer learning approach. Finally, classification and testing procedures were conducted on the obtained deep-effective properties using the SVM method. The multiclass TILDA dataset was used in order to test the proposed model. In experimental work, the MPF-DNN model for all four classes achieved a significant overall accuracy score of 95.82%. In the results obtained from extensive experimental studies, it was observed that the proposed MPF-DNN model was more successful than previous studies that used pretrained deep architectures.