使用卷积神经网络的无监督纺织品缺陷检测
Unsupervised textile defect detection using convolutional neural networks
AI总结：
针对纺织品缺陷检测，本文提出基于基序的无监督 CNN 方法，仅用一个无缺陷样本动态构建网络并检测异常，在 Patterned Fabrics 上以单 epoch 训练取得有竞争力的性能。
摘要：
在本研究中，我们提出了一种新颖的、基于基序的无监督纺织品异常检测方法，该方法结合了传统卷积神经网络与无监督学习范式的优势。它包含五个主要步骤：预处理、自动图案周期提取、图像块提取、特征选择和异常检测。所提出的方法采用一种新的动态启发式特征选择方法，避免了滤波器（神经元）数量及其权重初始化的缺陷，也避免了反向传播机制（如梯度消失）的缺陷，而这些在当前最先进方法中都很常见。网络的设计和训练以动态且基于输入域的方式进行，因此不需要临时配置。在构建模型之前，只需定义层数和步幅。我们既不像传统基于 CNN 的方法那样随机初始化权重，也不定义滤波器大小或滤波器数量。这减少了在超参数初始化和微调上花费的精力与时间。训练只需一个无缺陷样本，无需进一步的标注数据。训练后的网络随后用于检测有缺陷织物样本上的异常。我们在 Patterned Fabrics 基准数据集上证明了方法的有效性。与最先进的无监督方法相比，我们的算法在召回率、精确率、准确率和 F1 分数上取得了可靠且具有竞争力的结果，同时耗时更少，仅需单个 epoch 即可高效训练，且计算成本更低。
英文摘要：
In this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It consists of five main steps: preprocessing, automatic pattern period extraction, patch extraction, features selection and anomaly detection. This proposed approach uses a new dynamic and heuristic method for feature selection which avoids the drawbacks of initialization of the number of filters (neurons) and their weights, and those of the backpropagation mechanism such as the vanishing gradients, which are common practice in the state-of-the-art methods. The design and training of the network are performed in a dynamic and input domain-based manner and, thus, no ad-hoc configurations are required. Before building the model, only the number of layers and the stride are defined. We do not initialize the weights randomly nor do we define the filter size or number of filters as conventionally done in CNN-based approaches. This reduces effort and time spent on hyperparameter initialization and fine-tuning. Only one defect-free sample is required for training and no further labeled data is needed. The trained network is then used to detect anomalies on defective fabric samples. We demonstrate the effectiveness of our approach on the Patterned Fabrics benchmark dataset. Our algorithm yields reliable and competitive results (on recall, precision, accuracy and f1- measure) compared to state-of-the-art unsupervised approaches, in less time, with efficient training in a single epoch and a lower computational cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
