  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.
