An Examination of Algorithms for Target Detection and Their Application in Fabric Defect Detection Situations

https://elibrary.ru/rsxsqc

Abstract

Fabric defect detection is a significant area of research under the textile industry's growing trend toward automation and intelligence. Deep learning-based target identification algorithms have been applied extensively in the field of fabric detection in recent years, which has tremendously aided in the advancement of intelligence in the textile sector. The following factors are taken into consideration when analyzing the research state of YOLO series algorithms in the field of fabric flaw identification. It begins by summarizing the target detection development trend. Next, it summarizes and examines the structure and function of the YOLO family of algorithms. Finally, it talks about the use of YOLO algorithms and their derivatives in the field of fabric flaw identification and inspection. Lastly, it considers the issues and potential paths for target detection development in the future.