Automatic fabric defect detection using a deep convolutional neural network
Automatic fabric defect detection using a deep convolutional neural network
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DOI：
10.1111/cote.12394
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发表时间：
2019-06-01
影响因子：
1.8
通讯作者：
Zhang, Huan-Huan
中科院分区：
文献类型：
--
作者：
Jing, Jun-Feng;Ma, Hao;Zhang, Huan-Huan
织物疵点检测在纺织品生产过程中起着重要的作用，但如何快速准确地检测出织物疵点仍存在一些挑战。在本文中，我们提出了一种使用深度卷积神经网络（CNN）进行自动织物缺陷检测的强大检测方法。它包括三个主要步骤。首先，织物图像被分解成局部补丁和每个局部补丁标记。然后将标记的补丁传输到预训练的深度CNN进行迁移学习。最后，在检测阶段通过使用训练好的模型在整个图像上滑动来检测缺陷，并且获得每个缺陷的类别和位置。在两个公开的织物数据库和一个自制的织物数据库上对该方法进行了验证。实验结果表明，我们的方法显着优于选定的国家的最先进的方法在质量和鲁棒性。
Fabric defect detection plays an important role in the textile production process, but there are still some challenges in detecting defects rapidly and accurately. In this paper, we propose a powerful detection method for automatic fabric defect detection using a deep convolutional neural network (CNN). It consists of three main steps. First, the fabric image is decomposed into local patches and each local patch is labelled. Then the labelled patches are transmitted to the pretrained deep CNN for transfer learning. Finally, defects are detected during the inspection phase by sliding over the whole image using the trained model, and the category and position of each defect is obtained. The proposed method is validated on two public and one self-made fabric database. The experimental results demonstrate that our method significantly outperforms selected state-of-the art methods in terms of both quality and robustness.