Application of artificial neural network in determining the fabric weave pattern

Subrata Das, Keerthana Shanmugaraja
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引用次数: 0

Abstract

The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. The approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and occurs more errors. Hence, an automated system is needed for classification of woven fabric to improve productivity. Along with the rapid development of computer vision, the automatic and efficient methods for woven fabric classification are desperately needed. The prediction of fabric weave pattern Fabric is done by acquiring the high-quality images of the fabric. Then the acquired images are subjected to weave classification algorithm. The output of the processed image is used as an input to the Artificial Neural Network (ANN) which uses back propagation algorithm to calculate the weighted factors and generates the desired classification of weave patterns as an output. In this review paper discussed about the study on the various neural network that are used for prediction of fabric weave pattern.
人工神经网络在织物织型确定中的应用
机织物的组织花纹(肌理)被认为是设计和生产高质量织物的重要因素。传统的机织物识别由于需要人工目视检测,给识别带来了很大的挑战。基于早期机器学习算法的方法直接依赖于手工制作的特征,这既耗时又容易出错。因此,需要一个自动化系统来分类机织物,以提高生产率。随着计算机视觉技术的飞速发展,迫切需要一种自动、高效的机织物分类方法。织物的织型预测是通过获取织物的高质量图像来实现的。然后对采集到的图像进行编织分类算法。处理后的图像输出作为人工神经网络(ANN)的输入,人工神经网络使用反向传播算法计算加权因子,并生成所需的编织图案分类作为输出。本文综述了用于织物织型预测的各种神经网络的研究进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Zastita materijala
Zastita materijala Materials Science-General Materials Science
CiteScore
0.80
自引率
0.00%
发文量
26
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