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As a popular topic in automation, fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. The main challenge for automatically detecting fabric damage, in most cases, is the complex structure of the textile. This article presents a two-stage approach, combining novel and traditional algorithms to enhance image enhancement and defect detection. The first stage is a new combined local and global transform domain-based image enhancement algorithm using block-based alpha-rooting. In the second stage, we construct a neural network based on the modern architecture to detect fabric damage accurately. This solution allows localizing defects with higher accuracy than traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset.
Voronin et al. (Wed,) studied this question.