  Fabric is a planar material composed of textile fibers. Textile fibers are
generated from many natural sources; including plants, animals, minerals, and
even, it can be synthetic. A particular fabric may contain different types of
fibers that pass through a complex production process. Fiber identification is
usually carried out through chemical tests and microscopic tests. However,
these testing processes are complicated as well as time-consuming. We propose
FabricNet, a pioneering approach for the image-based textile fiber recognition
system, which may have a revolutionary impact from individual to the industrial
fiber recognition process. The FabricNet can recognize a large scale of fibers
by only utilizing a surface image of fabric. The recognition system is
constructed using a distinct category of class-based ensemble convolutional
neural network (CNN) architecture. The experiment is conducted on recognizing
50 different types of textile fibers. This experiment includes a significantly
large number of unique textile fibers than previous research endeavors to the
best of our knowledge. We experiment with popular CNN architectures that
include Inception, ResNet, VGG, MobileNet, DenseNet, and Xception. Finally, the
experimental results demonstrate that FabricNet outperforms the
state-of-the-art popular CNN architectures by reaching an accuracy of 84% and
F1-score of 90%.
