A fiber recognition framework based on multi-head attention mechanism

Abstract

Convolutional neural networks have been extensively studied in textile fiber recognition. However, the down-sampling performed by the convolutional and pooling layers on the extracted features result in a significant loss of fine-grained fiber features. To address this issue, a multi-head attention framework for fiber recognition has been proposed. First, textile surface images are captured using optical magnifiers and smart devices such as smartphones. Second, fiber features and label embeddings are learned separately by multi-head self-attention. Finally, the label embeddings are used to query the presence of fiber types, and pool type-related features in the multi-head cross-attention module to classify fibers. The experimental results demonstrate that the proposed method performs exceptionally well on the textile surface image dataset, with a mean average precision accuracy improvement of 6% compared with the convolutional neural network-based fiber recognition method Cu-Net, and a 5% improvement compared with the fiber recognition method FiberCT combining convolutional neural networks and attention mechanisms. It is worth mentioning that the fiber images captured at 200× magnification in the collected dataset are most favorable for models to recognize fiber. The proposed method achieves a mean average precision fiber recognition accuracy of 80.2% on images at 200× magnification.