Considering the reduction in labor costs and associated benefits, investment
in automated fabric defect detection proves to be highly cost-effective. A robust and
efficient algorithm is essential for the development of a fully automated web inspection
system. The examination of genuine fabric flaws is particularly difficult because of the
multitude of defect categories, which are defined by their ambiguity and indistinctness.
This work aims to classify and describe several strategies developed for detecting
fabric faults. This work also provides the inaugural survey on methodologies for fabric
defect detection, referencing around 160 sources. The categorization of fabric defect
detection methodologies is beneficial for assessing the characteristics of the identified
features. The characterization of authentic fabric surfaces through their structure and
primitive set has not yet demonstrated success. Consequently, the characteristics
derived from fabric surfaces have led to the classification of the proposed
methodologies into three categories: statistical, spectral, and model-based. To evaluate
the state-of-the-art, the constraints of several prospective techniques have been
identified, and their performance has been appraised based on demonstrated findings
and proposed applications. The results of this work indicate that integrating certain
statistical, spectral, and model-based methodologies may produce superior outcomes
compared to any individual strategy, warranting additional investigation into this
matter.
Keywords: Fabric defect detection, automation, detection accuracy, image representation, convolutional neural network.
