Bioinspired Visual-Integrated Model for Multilabel Classification of Textile Defect Images

Abstract

In modern textile industrial processes, textile defect recognition and classification are of vital importance for textile quality control. Recently, as the critical machine-learning method, the deep convolutional neural network (CNN) has shown superior performance in the single-label classification of textile defect images. However, accurately recognizing and classifying multilabel textile defect images are still an unsolved issue due to the complexity of intersected defects, as well as difficulty in distinguishing small-size defects and concerning the correlations amongst labels. To address these challenges, we propose a bioinspired visual-integrated model for multilabel classification of textile defects called BIVI-ML. Three bioinspired visual mechanisms (the visual gain mechanism, the visual attention mechanism, and the visual memory mechanism) are proposed and built within the BIVI-ML to: 1) enhance the resolution and feature discrimination; 2) attend to the textile defect; and 3) associate relevant labels. To evaluate the proposed method, a unique multilabel textile defect database is built as the benchmark for the multilabel classification of textile defect images. Compared with both the traditional and the state-of-the-art deep-learning classification methods, the proposed BIVI-ML achieves the best performance in both single-label and multilabel image classification.