Automated fabric defect detection—A review

This paper provides a review of automated fabric defect detection methods developed in recent years. Fabric defect detection, as a popular topic in automation, is a necessary and essential step of quality control in the textile manufacturing industry. In categorizing these methods broadly, a major group is regarded as non-motif-based while a minor group is treated as motif-based. Non-motif-based approaches are conventional, whereas the motif-based approach is novel in utilizing motif as a basic manipulation unit. Compared with previously published review papers on fabric inspection, this paper firstly offers an up-to-date survey of different defect detection methods and describes their characteristics, strengths and weaknesses. Secondly, it employs a wider classification of methods and divides them into seven approaches (statistical, spectral, model-based, learning, structural, hybrid, and motif-based) and performs a comparative study across these methods. Thirdly, it also presents a qualitative analysis accompanied by results, including detection success rate for every method it has reviewed. Lastly, insights, synergy and future research directions are discussed. This paper shall benefit researchers and practitioners alike in image processing and computer vision fields in understanding the characteristics of the different defect detection approaches.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (190 K)Download as PowerPoint slideResearch highlights► Review of automated fabric inspection methods in recent 20 years with 139 references. ► Significant features, pros and cons of each approach are discussed. ► It offers a wider categorization of methods of seven classes. ► A qualitative analysis (accuracy, quantity of samples, etc.) is made for each method.