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Fabric Defect Detection and Classification using YOLOv4

Lakshmi J. V. N. *, Ketan Malwa**

*Sunstone Eduversity, Bangalore, Karnataka, India.

**Senior Software Engineer, Bharti Airtel, India.

Periodicity:July - September'2021
DOI : https://doi.org/10.26634/jse.16.1.18465

Abstract

The textile industry places a high value on detecting and classifying the different kinds of defects that can be found on fabric surface. Using deep-learning and computer vision, the proposed study has detected and classified different fabric defects into their respective classes. This has been accomplished by using a specialized YOLOv4 image detection and classification model. The fabric images were engineered in the first step, then a pre-trained YOLOv4 model was used to learn the defects from the trained data, and in the second step its detection precision was measured on the test data. In the third stage, the model has been compared with YOLOv3 and YOLOv2. The result of this research will immensely help the fabric manufacturing companies to reduce the manual effort of detecting and classifying different sorts of fabric defects if the models are implemented on physical devices to detect the defects in real-time and production environments.

Keywords

YOLOv4, Fabric Detection, Image Processing, Defect Detection.

How to Cite this Article?

Lakshmi, J. V. N. and Malwa, K. (2021). Fabric Defect Detection and Classification using YOLOv4. i-manager's Journal on Software Engineering, 16(1), 1-14. https://doi.org/10.26634/jse.16.1.18465

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