Optimizing Woven Curtain Fabric Defect Classification using Image Processing with Artificial Neural Network Method at PT Buana Intan Gemilang

Shadika,; Mulyana, Tatang; Rendra, Meldi

doi:10.1051/matecconf/201713500052

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…A survey paper on defect detection of fabric by different types of neural classifiers is reported [14]. In one of the recent studies [15], extracted statistical features from knitted fabric in RBG color components and trained the two hidden layers of ANN.…”

Section: Introductionmentioning

confidence: 99%

Defect detection in textiles using back propagation neural classifier

Das,

Wahi,

Jayaram

2023

Zaštita materijala

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The textile products are affected by the defects during the manufacturing processes. It is also waste of the resources used for the production and in turn it affects the business. The manual inspection in defect detections is not encouraged these days in manufacturing process. The computer vision with machine learning algorithms in automated quality control system plays an important role in detecting defects in manufacturing process as well as analyzing the quality of products. Classification of defects in knitted fabric is an active area of research around the globe. This paper presents a classification method to detect defects such as holes and thick places in knitted fabric by applying artificial neural network algorithm. The artificial neural network algorithms learn from the input data after successful training process, it predicts the nature of the unknown samples in very fast and accurate way. The proposed work has been carried out in two phases. In the first phase the images of the defective samples of two classes were collected by a high-resolution camera. The color images of the samples were converted into grey scale images. The features were extracted from each grey scale image and stored in a database. In the second phase a neural classifier was trained with back-propagation neural Network (BPNN) algorithm on the training dataset. After successful training of the neural network on train dataset, the performance of the trained neural network was evaluated on the test dataset. Different experiments were carried out by increasing the number of training data samples; it was found that the best evaluation performance was obtained as 83.3%.

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Section: Introductionmentioning

confidence: 99%

Defect detection in textiles using back propagation neural classifier

Das,

Wahi,

Jayaram

2023

Zaštita materijala

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“…Currently, many approaches have been proposed to detect and classify the textile fabric defects. Generally, these approaches can be divided into two kinds: one is based on the traditional machine learning methods (Hu et al, 2015;Liu et al, 2019;Shadika et al, 2017;Qing et al, 2018;Jing et al, 2013;Wang et al, 2018), which are mainly classified the textile fabric patterns by extracting low-level features of textile fabric images, like Scale-Invariant Feature Transform (CSIFT) (Qing et al, 2018), Fourier transform, wavelet transform (Yang et al, 2002(Yang et al, , 2004(Yang et al, , 2005, Gaussian mixture model (GMM) (Yu et al, 2010) combining extreme learning machine (ELM) (Liu et al, 2019;Qing et al, 2018), support vector machine (SVM) (Qing et al, 2018;Li and Cheng, 2014)and k-nearest neighbors (KNN), Bayesian classifiers (Habib et al, 2016), Euclidean distance, clustering and classification method of defect image classification, these methods can effectively solve the problem of a specific classification of textile fabric defect image, but due to the complexity of textile fabric defect, limited data samples and shallow features, these methods have poor generalization ability. For example, (Yang et al, 2005) the adaptive wavelets-based approach was expanded from the use of a single adaptive wavelet to multiple adaptive wavelets.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning-based CNN for textile fabric defects classification

Zhao

Zhang

2021

IJCST

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PurposeClassifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.Design/methodology/approachTo improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.FindingsThe authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.Originality/valueThe ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

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