Fabric Defect Detection Based on Artificial Intelligence

Authors

  • Deageon Kim Department of Architectural Engineering, Dongseo University, Republic of Korea
  • Dongoun Lee Department of Architectural Engineering, Dongseo University, Republic of Korea

Keywords:

Fabric Detection, Textile Detection, Garments, Fabric Defect, Artificial intelligence

Abstract

Defect identification is crucial for maintaining product quality during the fabric production process. The use of artificial vision systems to automatically identify fabric flaws can better meet the needs of the manufacturing process if you take into account that the old manual methods for detecting fabric defects are time-consuming and inaccurate. We enhanced modified YOLO in this research to better detect fabric flaws. This technique boosts classification and detection performance without using more processing power. The outcomes of the experiment demonstrate how effectively with an attention module may increase classification accuracy. To boost recognition performance, focal loss function and central limitations are applied. Evaluations are done on the Fabric defect databases, which are accessible to the general public in kaggle. The obtained results show that the suggested technique works well when compared to other methods and has great fault detecting capability in the textile pictures gathered.

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Published

24.11.2023

How to Cite

Kim, D. ., & Lee, D. . (2023). Fabric Defect Detection Based on Artificial Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 01–07. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3814

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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