  Quality control at each stage of production in textile industry has become a
key factor to retaining the existence in the highly competitive global market.
Problems of manual fabric defect inspection are lack of accuracy and high time
consumption, where early and accurate fabric defect detection is a significant
phase of quality control. Computer vision based, i.e. automated fabric defect
inspection systems are thought by many researchers of different countries to be
very useful to resolve these problems. There are two major challenges to be
resolved to attain a successful automated fabric defect inspection system. They
are defect detection and defect classification. In this work, we discuss
different techniques used for automated fabric defect classification, then show
a survey of classifiers used in automated fabric defect inspection systems, and
finally, compare these classifiers by using performance metrics. This work is
expected to be very useful for the researchers in the area of automated fabric
defect inspection to understand and evaluate the many potential options in this
field.
