Visual Saliency Guided Deep Fabric Defect Classification

Abstract

Fabric defects have an important influence on the quality of the fabric product. Automatic fabric defect detection is a crucial part for quality control in the textile industry. The primary challenge of fabric defects identification is not only to find the existing defects, but also to classify them into different types. In this paper, we propose a novel fabric defect detection and classification method consists of three main steps. Firstly, the fabric image is cropped into a set of image patches and each patch is labeled with specified defect type. Secondly, the visual saliency map is generated from the patch to localize defects with specified visual attention. Then, the combination of visual salience map with raw image input into a convolutional neural network for robust feature representation, and finally output its predicted defect type. During the testing section, defect inspection runs in a sliding window schemes using the trained model, and both the type and position of each defect are obtained simultaneously. Our method tries to investigate the combination of visual saliency and one-stage object detector with feature pyramid, which fully makes use of information from multi-resolution guided with visual attention. Besides, soft-cutoff loss is employed to further improve the performance of the method, and our network can be learnt in an end-to-end manner. Experiments based on our fabric defect image datasets, the proposed method can achieve a 98.52% accuracy of classification. This method is comparable to the usual two-stage detector with more compact model parameters, makes it valuable in the industrial application.