Jute Fabric Defects by Sujai Das, Surajit Sengupta (classic children's novels .txt) 📕

Using neural networks as a classifier requires two phases –a training phase and atesting phase. In the training phase, the neural network makes the proper adjustment for itsweights (W) to produce the desired response. When the actual output response is the same asthe desired one, the network has completed the training phase (i.e. it has acquired knowledge).In the testing phase the neural network is asked to classify a new set of images and its successis evaluated. In this work the neural networks were trained by the “backpropagation”algorithm to detect and classify the weaven fabric defects. The feature vectors were used asthe input vectors to the Neural Network.

The performance of the proposed fabric defect detection scheme is evaluated byusing a set of fabric images chosen from the Manual of Standard Fabric Defects in the textileIndustry. These images are captured by a digital camera. There are some defect-free images inthe database. The other images contain different types of fabric defects.The fabrics in the database are mainly plain weaving fabric, although other types offabrics are also included. In this paper, we select four types of defects images and all theimages have a size of 300x300 pixels and an 8-bit gray level. And we select four imagesdealing results which are shown in the following figure. The number of neural nodes of theinput layer was four corresponding to the four characteristic variables. The number of neuralnodes of the output layer was two so that the output values, i.e. 00,01,10,and 11,correspondedto the four classes of samples images .

A new intelligent fabric defect inspection model was presented. Therecognizer acquires digital fabric images by image acquisition device and converts that imageinto binary image by restoration and threshold techniques. The output of the processed imageis used as an input to the Neural Network (NN) which uses back propagation algorithm tocalculate the weighted factors and generates the desired classification of defects as an output.The experiment results show that the proposed method is feasible and applicable in textileproduction factories for defect detection and classification.

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Publication Date: 02-23-2015

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