Unifying Convolution and Transformer Decoder for Textile Fiber Identification  

作　　者：许罗力 李粉英 常姗 XU Luoli;LI Fenying;CHANG Shan(College of Computer Science and Technology,Donghua University,Shanghai 201620,China;Silicon Engineer Group,ZEKU Technology(Shanghai),Shanghai 201203,China)

机构地区：[1]College of Computer Science and Technology,Donghua University,Shanghai 201620,China [2]Silicon Engineer Group,ZEKU Technology(Shanghai),Shanghai 201203,China

出　　处：《Journal of Donghua University(English Edition)》2023年第4期357-363,共7页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.61972081);Fundamental Research Funds for the Central Universities,China(No.2232023Y-01);Natural Science Foundation of Shanghai,China(No.22ZR1400200)。

摘　　要：At present,convolutional neural networks(CNNs)and transformers surpass humans in many situations(such as face recognition and object classification),but do not work well in identifying fibers in textile surface images.Hence,this paper proposes an architecture named FiberCT which takes advantages of the feature extraction capability of CNNs and the long-range modeling capability of transformer decoders to adaptively extract multiple types of fiber features.Firstly,the convolution module extracts fiber features from the input textile surface images.Secondly,these features are sent into the transformer decoder module where label embeddings are compared with the features of each type of fibers through multi-head cross-attention and the desired features are pooled adaptively.Finally,an asymmetric loss further purifies the extracted fiber representations.Experiments show that FiberCT can more effectively extract the representations of various types of fibers and improve fiber identification accuracy than state-of-the-art multi-label classification approaches.

关 键 词：non-destructive textile fiber identification transformer decoder asymmetric loss 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]