A contrastive learning-based attention generative adversarial network for defect detection in colour-patterned fabric

The pattern style of colour-patterned fabrics is varied. Defective fabric samples are scarce in the production of small batches of colour-patterned fabrics. Therefore, the unsupervised defect-detection method for colour-patterned fabric has attracted wide attention. Several unsupervised defect-detection methods for colour-patterned fabrics based on convolutional neural networks have been proposed. However, convolutional neural network methods cannot learn long-range semantic information interaction well because of the intrinsic locality of convolution operations. Besides, as the number of layers in the convolutional neural network increases, the feature maps become more and more complex. Convolutional neural networks experience difficulties in coordinating numerous parameters and extracting key features from complex feature maps. Both these problems reduce the accuracy of the model for detecting defects in colour-patterned fabrics. In this paper, we propose a Contrastive Learning-based Attention Generative Adversarial Network (CLAGAN) for defect detection in colour-patterned fabrics. The CLAGAN possesses two important parts: contrastive learning and a channel attention module. Contrastive learning captures long-range dependencies by calculating the cosine similarity between different features. The channel attention module assigns different weights to each channel of the feature maps, and it enables the model to extract key features from those feature maps. The experimental results verified the effectiveness of the CLAGAN. It obtained values of 38.25% for intersection over union and of 51.67% for the F1-measure on the YDFID-2 public dataset.

Automated summary

The unsupervised defect-detection method for colour-patterned fabric has attracted wide attention. In this paper, a Contrastive Learning-based Attention Generative Adversarial Network (CLAGAN) is proposed for defect detection in colour-patterned fabrics, which effectively improves the detection accuracy.