  Automatic defect detection is a challenging task because of the variability
in texture and type of fabric defects. An effective defect detection system
enables manufacturers to improve the quality of processes and products.
Automation across the textile manufacturing systems would reduce fabric wastage
and increase profitability by saving cost and resources. There are different
contemporary research on automatic defect detection systems using image
processing and machine learning techniques. These techniques differ from each
other based on the manufacturing processes and defect types. Researchers have
also been able to establish real-time defect detection system during weaving.
Although, there has been research on patterned fabric defect detection, these
defects are related to weaving faults such as holes, and warp and weft defects.
But, there has not been any research that is designed to detect defects that
arise during such as spot and print mismatch. This research has fulfilled this
gap by developing a print fabric database and implementing deep convolutional
neural network (CNN).
