Fabric defect classification using prototypical network of few-shot learning algorithm

Computer-vision technology plays a vital role in automated fabric defect classification. In this paper, a novel prototypical network is presented for improving the fabric defect classification performance, especially in the case of an imbalanced distribution over the number of class samples. A traditional neural network (such as a convolutional neural network) usually inputs a batch of samples each time in training until the entire training dataset is covered, and thus it is not robust to cope with imbalanced data. The proposed network follows an N-way K-shot paradigm to split the training set into a support set and query set, and thereby forces the number of samples within each class to be uniformly distributed. The support set is used to learn the common knowledge of each class, whereas the query set is utilized to fine-tune the model parameters gained from the respective support set. The prototype of each class in the support set is computed as the representation of the class. For samples in the query set, the loss function is designed to match them with the corresponding prototypes as accurately as possible. In addition, the class activation mapping is used to visualize and interpret the discriminative regions of interest most relevant to specific defect classes. The classification performance of the proposed method is tested against five existing models on a labeled dataset of fabric defect images collected by a commercial inspection system. The proposed method achieves the highest classification accuracy (96.04%) over seven defect categories among the six tested methods.