Fabric defect detection plays an essential role in the textile production process, which was widely applied in the textile industry. For fabric defect detection, many algorithms have been proposed. However, lots of important problems, such as the accuracy of detection, the computational complexity of the algorithm, and data imbalance, still needed to be addressed for application in industrial production. In this article, we propose an efficient convolutional neural network for defect segmentation and detection. The design of this framework significantly alleviates the manual annotation cost of the data set; it only needs few defect samples combined with standard samples to learn the potential feature of defects and obtain the location of defects with high accuracy. The network is divided into two parts: segmentation and decision. First, the fabric data set without training is utilized as the input of the segmentation network. Then, the output of the segmentation network is applied as the raw materials for training the decision network. Finally, a well-trained network is used to obtain the location of defects with high accuracy. The proposed method only demands almost 50 defect samples to get accurate segmentation results and can achieve the requirement of real-time detection with a speed of 25 frames per second (FPS). The experimental results based on a public data set and three self-made fabric data sets show that the proposed method significantly outperforms eight state-of-the-art methods in terms of accuracy and robustness.

中文翻译：

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基于深度学习的织物疵点分割方法


织物疵点检测在纺织生产过程中起着至关重要的作用，在纺织行业中得到了广泛的应用。对于织物缺陷检测，已经提出了许多算法。然而，在工业生产中的应用仍然需要解决许多重要问题，例如检测的准确性、算法的计算复杂性、数据不平衡等。在本文中，我们提出了一种用于缺陷分割和检测的高效卷积神经网络。该框架的设计显着减轻了数据集的人工标注成本；只需要少量的缺陷样本与标准样本相结合即可学习缺陷的潜在特征并获得高精度的缺陷位置。网络分为两部分：分段和决策。首先，利用未经训练的织物数据集作为分割网络的输入。然后，分割网络的输出被用作训练决策网络的原材料。最后，使用训练有素的网络来获得高精度的缺陷位置。该方法仅需要近50个缺陷样本即可获得准确的分割结果，并且可以达到每秒25帧（FPS）的实时检测要求。基于公共数据集和三个自制织物数据集的实验结果表明，该方法在准确性和鲁棒性方面显着优于八种最先进的方法。