Innovative Fabric Defect Detection Strategies for Enhanced Textile Quality

Fabric defect detection is a crucial step in the textile industry's quality assurance process. Traditional techniques, such as human inspection, were employed in the past to find fabric defects. However, there are several drawbacks to this approach, including its time-consuming nature and lower accuracy and efficiency. Research on image application has turned its attention to the use of digital image processing to identify fabric flaws because of advancements in computer vision technology. When considering the implementation of a fully automated online inspection system, robust, real-time, and effective fault detection algorithms are required. Numerous techniques have been developed to identify material defects, and this paper aims to explain these techniques. The examination of real material defects is particularly challenging due to the wide range of material defect categories that are characterized by their incomprehensibility. This article proposes a fabric defect detection method based on YOLOv5 and is compared with the experimental results using YOLOv7 under the same conditions.